Join Our 100% Job Guaranteed

Gen AI Course in Anna Nagar

Join the Best Gen AI Training Institute in Anna Nagar to Master Artificial Intelligence Concepts and Techniques.
Our Gen AI Training in Anna Nagar Covers Topics such as ML, DL, Natural Language Processing and Computer Vision.
Learn with Flexible Options: Weekday, Weekend or Fast-track Batches.
Get Hands-on Experience by Working on Live Projects, Guided by Industry Experts.
Earn a Globally Recognized Gen AI Certification with Job Placement Assistance.
Get Expert Support in Crafting a Winning Resume and Acing AI Job Interviews.

WANT IT JOB

Become a Generative AI Engineer in 3 Months

Freshers Salary

3 LPA

8 LPA

Quality Training With Affordable Fees in Anna Nagar!

INR ~~₹23000~~

INR ₹22000

10384+

(Placed)

Freshers To IT

5362+

(Placed)

NON-IT to IT

7673+

(Placed)

Career Gap

4232+

(Placed)

Less Then 60%

550+ Students Placed Every Month Be The Next!

Our Hiring Partners

Overview of the Gen AI Course

The Gen AI Course in Anna Nagar is designed for freshers to learn the fundamentals of Generative AI in a simple and practical way. This Gen AI Training in Anna Nagar covers key concepts, tools and real-time project work to help students gain hands-on experience. Participants can explore Gen AI Internships in Anna Nagar to apply their skills in real-world scenarios. The course also guides students on career opportunities and Gen AI Placement support to start their professional journey. With easy-to-understand lessons and practical exercises, freshers can confidently build and deploy AI models. Overall, this course is perfect for beginners who want to grow their career in Gen AI.

What You'll Learn From Gen AI Certification Course in Anna Nagar

Gain a strong foundation in Gen AI concepts, including ML, DL and natural language processing, with easy-to-understand lessons.

Develop essential skills in data preprocessing, feature engineering and model building for AI applications, ensuring a hands-on, practical approach.

Work on real-time projects and case studies relevant to the AI industry, providing you with valuable experience and insights into current AI trends.

Master key AI tools and technologies, such as TensorFlow, Keras and Python, to build your confidence in creating intelligent systems.

Learn the process of data analysis and decision-making, enabling you to apply AI solutions to real-world business problems.

Receive expert guidance throughout the Gen AI Training in Anna Nagar, Assisting you in obtaining an industry-recognized certification and opening the door to interesting employment prospects.

Additional Info

Course Highlights

Kickstart your Gen AI journey by mastering AI concepts like machine learning, deep learning, NLP and data analysis all in one comprehensive course.
Get dedicated job support with placement opportunities from top companies actively hiring Gen AI professionals.
Join over 10,000+ students who have been successfully trained and placed through our strong network of 300+ hiring partners.
Learn from qualified educators with over a decade of industry expertise in artificial intelligence and machine learning.
Enjoy beginner-friendly lessons, hands-on projects and full career guidance to ensure you succeed in the AI field.
Benefit from affordable course fees, flexible batch options and 100% placement assistance perfect for freshers and career switchers.

Benefits You Gain from an Gen AI Training

Enhanced Automation – Gen AI helps automate repetitive tasks, saving time and reducing human error. With AI tools, businesses can handle large volumes of data and processes quickly and efficiently. This boosts productivity and enables staff to concentrate on more complicated jobs. Automation through Gen AI streamlines operations across industries like healthcare, finance and manufacturing.
Data-Driven Insights – Gen AI assists organizations in analyzing enormous sets of data to identify patterns and trends that people may overlook. It facilitates more informed decision-making by offering real-time, data-driven insights. AI systems forecast results and make recommendations based on past data by employing machine learning algorithms. The capacity to process and interpret data improves strategic planning and forecasting.
Personalized Experiences – AI can create customized experiences for users, whether its personalized recommendations, targeted marketing or individualized customer support. By understanding user preferences and behavior, AI systems tailor services to meet specific needs. Particularly in industries like e-commerce, entertainment and online services this results in increased client happiness and loyalty.
Cost Efficiency – Gen AI can help reduce costs by streamlining operations and improving efficiency. Tasks that once required large teams of people can now be automated with AI, cutting down labor costs. Additionally, AI can optimize resource management and reduce waste, leading to savings. Businesses can achieve higher profits while maintaining or improving service quality.
Advanced Problem Solving – Gen AI equips businesses with powerful tools to solve complex problems. By applying advanced algorithms, AI can analyze problems from various angles and come up with innovative solutions. This capability is particularly helpful in industries like healthcare, where AI may help with disease diagnosis and patient outcome prediction. AI’s problem-solving capabilities enhance innovation and improve performance.

Important Tools Covered in Gen AI Course in Anna Nagar

TensorFlow – Google created TensorFlow, an open-source platform for creating and refining machine learning models. It is extensively utilized for deep learning applications such as natural language processing, picture and audio recognition and more. TensorFlow provides an easy way to design complex neural networks and is highly scalable, allowing it to be used for both small projects and large-scale applications. Its flexibility makes it a popular tool for both beginners and experts in the AI field.
Keras – Keras is high-level neural networks API that runs on top of TensorFlow, making it easier to build deep learning models. It simplifies the process of designing and testing AI models with simple, intuitive code. Keras is ideal for beginners because of its user-friendly interface, but also powerful enough for professionals to create complex models. Its commonly used for tasks like image classification text processing and more.
OpenAI GPT – A language model called OpenAI GPT (Generative Pretrained Transformer) was created to comprehend and produce text that is similar to that of a person. Chatbots, content creation and language translation are just a few of the uses for it. Based on the input given, GPT predicts the subsequent word or phrase in a sentence. Because of its ability to produce coherent and contextually relevant content it is one of the most crucial tools in natural language processing.
PyTorch – Facebook's AI Research lab created the open-source machine learning library PyTorch. It is especially well-liked because of its dynamic computational graph which facilitates machine learning model experimentation. PyTorch is used extensively in academia and industry for tasks like computer vision, natural language processing and reinforcement learning. Its simplicity and flexibility make it ideal for rapid prototyping and deep learning research.
Hugging Face Transformers – Hugging Face Transformers is a robust natural language processing library that offers pre-trained models for a variety of tasks, including summarization, translation and text production. It allows developers to quickly integrate state-of-the-art AI models into their applications without having to train models from scratch. The library is easy to use, even for beginners and has a strong community for support. Hugging Face has become a go-to tool for working with large-scale language models like GPT and BERT.

Top Frameworks Every Gen AI Should Know

TensorFlow – One of the most widely used frameworks for creating deep learning and machine learning models is TensorFlow. Developed by Google, it offers a comprehensive ecosystem for developing, training and deploying AI models across platforms. You may work on a wide range of AI applications with TensorFlow, including natural language comprehension, audio processing and image identification. Its flexibility and scalability make it ideal for both beginners and advanced AI practitioners, supporting a range of projects from small apps to large-scale production systems.
PyTorch – PyTorch is a dynamic deep learning framework developed by Facebook’s AI Research lab. Unlike TensorFlow, PyTorch uses a more intuitive approach with dynamic computation graphs, which makes it easier to debug and experiment with models. It’s widely used in research and development because of its simplicity and speed, especially for tasks involving computer vision and natural language processing. Many top researchers and developers prefer PyTorch for its flexibility and ease of use in AI projects.
Keras – TensorFlow and Theano are two frameworks on which Keras, a high-level neural network API, operates. Because of its quick and easy-to-use architecture, its a great option for AI novices. Keras simplifies the process of designing, training and deploying deep learning models, so developers can focus more on the problem-solving aspect rather than the technical details. Despite being beginner-friendly, it is powerful enough for professionals to build sophisticated AI applications.
Apache MXNet – The open-source deep learning framework Apache MXNet is the renowned for its effectiveness and scalability. Its especially popular for applications in large-scale AI systems and cloud computing. MXNet supports both symbolic and imperative programming, which makes it versatile for different AI tasks. It also integrates well with other big data technologies and is often used for deploying AI models in production environments with high-speed performance.
Hugging Face Transformers – Hugging Face Transformers is a powerful framework designed for natural language processing (NLP) tasks. It offers pre-trained models that are ready to use for tasks like text classification, translation, question answering and more. The library makes it easy to integrate state-of-the-art models such as GPT, BERT and T5 into your AI applications without needing to train them from scratch. With its user-friendly interface for both novices and specialists, Hugging Face has swiftly emerged as the preferred tool for developers working with huge language models.

Essential Skills You’ll Learn in a Gen AI Training

Machine Learning Basics – The foundations of machine learning, the foundation of AI systems, will be covered in a Gen AI course. This entails comprehending methods such as support vector machines, decision trees and linear regression. Additionally, you will obtain practical experience in training models, assessing their performance and optimizing them to produce more accurate forecasts. This ability will enable you to create AI models that are capable of learning from data and getting better over time.
Deep Learning Techniques – Multi-layered neural networks are the focus of the machine learning subject known as "deep learning." You will learn to build and train the deep neural networks for applications like image identification, speech recognition and natural language processing. This skill involves working with tools like TensorFlow and PyTorch to create complex models that can handle large datasets and learn intricate patterns. Mastering deep learning is essential for anyone serious about advancing in AI.
Natural Language Processing (NLP) – Natural Language Processing (NLP) enables AI to understand and generate human language. In a Gen AI course, you’ll learn to process and analyze text data to create chatbots, recommendation systems and sentiment analysis tools. You’ll work with NLP models such as BERT and GPT, which are capable of translating languages, summarizing text and answering questions. This skill is crucial as NLP is widely used in applications like voice assistants and automated customer support.
Data Preprocessing and Feature Engineering – Feature engineering and data pretreatment are essential abilities for getting raw data ready for machine learning models. You’ll learn to clean, normalize and transform data so that it can be effectively used for training AI systems. This includes handling missing values, scaling features and encoding categorical variables. These techniques help improve the accuracy of models and ensure that AI applications can work with real-world, messy data.
AI Model Deployment – Once you’ve trained an AI model, you need to deploy it in real-world applications. In a Gen AI course, you’ll learn how to deploy models to the cloud or integrate them into applications using frameworks like Flask or FastAPI. You’ll also explore how to monitor models post-deployment and update them with new data. This skill is vital for turning AI projects into practical tools that can provide value to businesses or end users.

Key Roles and Responsibilities of Gen AI Profession

AI Research Scientist – AI Research Scientists focus on advancing the theory and techniques of artificial intelligence. Their responsibilities include developing new algorithms, models and approaches to solve complex AI challenges. They frequently work with cutting-edge technologies like natural language processing, deep learning and reinforcement learning. A key part of their role is publishing research findings, collaborating with academic institutions and pushing the boundaries of AI capabilities.
Machine Learning Engineer – Machine Learning Engineers design, build and deploy machine learning models in real-world applications. They are responsible for selecting the right algorithms, optimizing models and ensuring that AI systems perform at scale. Their work involves coding, data analysis and constantly tuning models to improve accuracy and efficiency. They also collaborate with data engineers to ensure proper data pipelines are in place for model training.
Data Scientist – Data Scientists in Gen AI analyze large datasets to extract insights and develop predictive models. They apply statistical methods, machine learning and data visualization techniques to understand trends, patterns and relationships within the data. Their role requires expertise in data cleaning, feature engineering and model evaluation. Data scientists often translate complex data into actionable insights that guide business decisions.
AI Product Manager – AI Product Managers serve as a link between business objectives and the advancement of AI technology. They are responsible for defining the vision and roadmap for AI-powered products, working closely with engineers, data scientists and stakeholders. Their role includes identifying customer needs, setting project priorities and ensuring AI solutions are aligned with business objectives. They also oversee the product’s lifecycle, ensuring its successful deployment and ongoing improvement.
AI Software Developer – AI Software Developers focus on building applications and systems that integrate AI algorithms and models. They write code to implement AI features, ensuring that software runs efficiently and meets user requirements. Their responsibilities include collaborating with machine learning engineers to integrate models, maintaining the performance of AI systems and continuously improving the software. They also focus on ensuring that the AI components of the software are reliable and scalable for real-world use.

Why Gen AI is the Smart Choice for Freshers

High Demand for AI Professionals – The demand for AI skills is growing rapidly across industries like healthcare, finance, retail and technology. Many companies are looking for fresh talent to develop and implement AI solutions. Entering the AI area opens doors to a variety of job prospects as businesses rely more and more on AI to boost productivity and spur innovation. For freshers, this means a strong job market with plenty of career paths to explore.
Lucrative Salary Potential – AI professionals, especially those with Gen AI skills, are often offered high-paying salaries. The expertise required to develop AI solutions is highly valued, making AI roles some of the most well-compensated positions in tech. Freshers who enter the AI field early can expect attractive starting salaries, along with opportunities for significant salary growth as they gain experience. The financial rewards are a major draw for those starting their careers.
Opportunity to Work on Cutting-Edge Technology – Gen AI training provides freshers the chance to work with some of the most advanced technologies available today. Whether its machine learning, deep learning or natural language processing AI opens up opportunities to engage with innovative solutions that are transforming industries. Being part of the AI revolution allows freshers to build skills in the latest technologies and contribute to shaping the future.
Wide Range of Career Paths – Gen AI training offers a wide variety of career options, including roles like machine learning engineer, data scientist, AI developer and AI product manager. Freshmen can select a route in research, development or implementation that best suits their interests and strengths because to this flexibility. With AI applications spanning multiple industries, the career opportunities are diverse and offer long-term growth potential.
Continuous Learning and Growth – AI is a rapidly evolving field, which means there’s always something new to learn. Freshmen have the opportunity to learn new skills and stay on cutting edge of technology due to this ongoing evolution. Engaging with AI means being part of a community that values innovation and ongoing learning, which ensures continuous professional development and career advancement in an exciting, dynamic field.

Landing Remote Jobs with Gen AI Skills

Flexibility in Job Roles – Numerous remote work opportunities in a variety of industries, including healthcare, banking and e-commerce are made possible by Gen AI expertise. Whether working as a data scientist, machine learning engineer or AI researcher these roles can often be done from anywhere. AI professionals can collaborate remotely with teams around the world, making it easier to find flexible, location-independent positions. Remote jobs in AI provide more freedom to work from home or travel while pursuing a successful career.
High Demand for AI Professionals – The demand for AI talent is rising globally and many companies are now open to hiring remote employees with Gen AI skills. Businesses looking to implement AI solutions are not restricted by location when searching for skilled professionals, meaning freshers can land remote jobs even in competitive markets. AI's widespread application across sectors ensures that remote work opportunities in this field are abundant and continually growing.
Access to Global Job Market – With Gen AI skills, freshers can tap into a global job market for remote positions. Remote roles allow professionals to apply for jobs in different countries without needing to relocate. This means that AI talent has access to a broader selection of companies and industries, improving their chances of finding the ideal remote job. Working remotely also opens the door to earning higher salaries from companies located in regions with strong tech economies.
Collaboration Through Cloud and AI Tools – Gen AI professionals often use cloud platforms and AI tools, which are designed to support remote collaboration. Tools like GitHub, Google Colab and TensorFlow allow AI teams to share code, track progress and work on projects from anywhere in the world. These cloud-based solutions ensure that AI projects proceed smoothly in a remote context by making it simple to stay in touch with team members, regardless of their location.
Growing Remote AI Startups – Many startups focused on AI and machine learning are operating remotely, offering freshers the chance to work in a dynamic environment. Gen AI skills are crucial for these companies, as they rely on innovative AI solutions to differentiate themselves in competitive markets. Startups often prefer hiring remote workers with AI expertise due to the flexibility and cost-effectiveness it offers. Joining a remote AI startup can provide exciting opportunities for growth, learning and contributing to cutting-edge projects.

What to Expect in Your First Gen AI Job

Hands-On Learning and Projects – In your first Gen AI job, expect to spend a lot of time applying what you’ve learned through hands-on projects. These could involve training machine learning models, working with datasets or developing AI applications. As a beginner you may start with smaller, manageable tasks to build your confidence and gradually take on more complex challenges. This real-world experience will help reinforce your skills and make learning more practical.
Collaborating with Experienced Teams – At the start of your Gen AI career, collaboration with more experienced team members is common. Data scientists, engineers and product managers will work closely with you, guiding you through tasks and assisting you in becoming better. Communication skills will be important as you'll need to explain your ideas clearly and ask questions when needed. This teamwork environment will allow to learn quickly and gain valuable insights from seasoned professionals.
Exposure to Cutting-Edge AI Tools – Expect to work with some of the latest AI frameworks and tools like TensorFlow, PyTorch and natural language processing libraries. These tools will become a key part of your day-to-day work, helping build and optimize AI models. While it may take time to get fully comfortable with these tools, your first job will provide the opportunity to get hands-on experience and improve your technical proficiency in the AI field.
Continuous Learning and Development – AI is a constantly evolving field and in your first Gen AI job, you'll find that learning is a continuous process. New techniques, algorithms and best practices emerge regularly, so staying up-to-date is essential. Expect to spend time on self-learning through online courses, workshops or internal training sessions provided by your company.
Problem Solving and Real-World Applications – In your first Gen AI role, much of your work will involve solving real-world problems using AI. You’ll apply AI techniques to improve business processes, optimize systems or create new applications that directly impact the company's performance. This hands-on approach to problem-solving will give you a sense of accomplishment and show you how your work makes a tangible difference. As you gain experience, you’ll also be given more responsibility in tackling complex challenges.

Top Companies are Hiring for Gen AI Professionals

Google – The company employs Gen AI experts to work on initiatives that affect millions of users globally, such as creating cutting-edge AI models or enhancing search engines. The company employs Gen AI experts to work on initiatives that affect millions of users globally, like creating cutting-edge AI models and enhancing search engines. Google offers AI specialists the opportunity to work on some of the most advanced technologies in the field. Employees benefit from a dynamic work environment, innovative projects and continuous learning opportunities.
Microsoft – Microsoft is heavily invested in AI with its Azure cloud platform offering powerful tools for building and deploying machine learning models. The company employs AI specialists to work on a range of projects, from improving cloud services to creating AI solutions for sectors like banking and healthcare. With a focus on ethical AI, Microsoft provides a great environment for Gen AI professionals to contribute to impactful, large-scale solutions. The company also offers a range of training resources to help employees grow in the AI field.
Amazon – Amazon uses AI extensively across its platforms, from personalized recommendations on Amazon.com to AI-powered logistics in its warehouses. The company is always looking for AI professionals to help improve its cloud services (AWS), automate operations and enhance customer experiences. Working at Amazon provides AI experts the chance to work on high-impact projects in retail, supply chain and cloud computing. The fast-paced environment ensures constant innovation and growth for professionals in the AI space.
IBM – IBM has led the way in artificial intelligence and its Watson AI platform is becoming well-known for its ML and natural language processing skills. The company hires Gen AI professionals to work on a range of enterprise-level solutions, from healthcare diagnostics to business automation. IBM offers AI specialists the chance to contribute to innovative projects that help businesses solve real-world problems. Professionals have access to the newest AI tools and technology because to the company's significant emphasis on research and development.
NVIDIA – NVIDIA is key player in the AI and machine learning field, particularly known for its graphics processing units (GPUs), which power many AI applications. The company hires Gen AI professionals to work on AI-driven research, autonomous vehicles, deep learning platforms and more. NVIDIA provides an exciting opportunity for AI experts to work on cutting-edge technologies used across industries like gaming, healthcare and automotive. The company fosters a culture of innovation, making it an ideal environment for AI professionals to grow and explore new possibilities.

Upcoming Batches For Classroom and Online

Weekdays

22 - Dec - 2025

08:00 AM & 10:00 AM

Weekdays

24 - Dec - 2025

08:00 AM & 10:00 AM

Weekends

27 - Dec - 2025

(10:00 AM - 01:30 PM)

Weekends

28 - Dec - 2025

(09:00 AM - 02:00 PM)

Can't find a batch you were looking for?

INR ₹22000

INR ~~₹23000~~

OFF Expires in

BOOK A FREE DEMO CLASS

Who Should Take a Gen AI Certification Course

IT Professionals

Non-IT Career Switchers

Fresh Graduates

Working Professionals

Diploma Holders

Professionals from Other Fields

Salary Hike

Graduates with Less Than 60%

Job Roles For Gen AI Course

Gen AI Engineer

AI Developer

AI Consultant

MLOps Engineer

AI Security Specialist

Gen AI Architect

AI Infrastructure Manager

AI Insights Specialist

Tools Covered For Gen AI Course in Offline

What’s included ?

📊 Free Aptitude and Technical Skills Training

Learn basic maths and logical thinking to solve problems easily.
Understand simple coding and technical concepts step by step.
Get ready for exams and interviews with regular practice.

🛠️ Hands-On Projects

Work on real-time projects to apply what you learn.
Build mini apps and tools daily to enhance your coding skills.
Gain practical experience just like in real jobs.

🧠 AI Powered Self Interview Practice Portal

Practice interview questions with instant AI feedback.
Improve your answers by speaking and reviewing them.
Build confidence with real-time mock interview sessions.

🎯 Interview Preparation For Freshers

Practice company-based interview questions.
Take online assessment tests to crack interviews
Practice confidently with real-world interview and project-based questions.

🧪 LMS Online Learning Platform

Explore expert trainer videos and documents to boost your learning.
Study anytime with on-demand videos and detailed documents.
Quickly find topics with organized learning materials.

Gen AI Course Syllabus

🏫 Classroom Training
💻 Online Training
🚫 No Pre Request (Any Vertical)
🏭 Industrial Expert

Our Gen AI Course in Anna Nagar, available both online and in-person, offers a comprehensive syllabus tailored for beginners and aspiring AI developers. You'll learn core Gen AI concepts, including machine learning, deep learning, natural language processing and AI frameworks like TensorFlow and PyTorch. Gain practical experience through Gen AI Internships and real-time projects, ensuring hands-on learning. The course also covers API integration, data analysis and model deployment basics. Additionally, we provide dedicated placement support to help with resume building, interview preparation and job placement assistance.

Introduction to Gen AI – Gain a solid foundation in AI by understanding the fundamentals of natural language processing, deep learning and machine learning.
Advanced AI Techniques – Explore advanced techniques and frameworks like TensorFlow, Keras and PyTorch for building AI models.
Hands-On Project – Work on real-world projects like image recognition and chatbots to gain practical experience.
AI Tools & Deployment – Learn to deploy AI models using tools like Jupyter, Git and Docker for cloud integration.

Introduction to Gen AI

Core Gen AI Services

Gen AI Administration Training

Gen AI Development Training

AI Automation & MLOps

Gen AI Analytics & Monitoring

Gen AI Strategy & Real-Time Projects

This module provides foundational knowledge of Generative AI:

Importance of Gen AI: Benefits, industry adoption, and business impact.
Core Concepts: Natural Language Processing, Computer Vision, Large Language Models, Automation, and Multimodal AI.
Key Platforms: Overview of OpenAI APIs, Hugging Face, LangChain, TensorFlow, and PyTorch.
Career Paths: Roles such as Gen AI Engineer, AI Developer, MLOps Engineer, and AI Security Specialist.

This module covers essential Gen AI tools and functionalities:

AI Administration: Managing datasets, model versions, pipelines, and resource allocation.
Data Management: Configuring embeddings, vector databases, training datasets, and ensuring data quality.
Model Configuration: Fine-tuning LLMs, setting up workflows with LangChain, and automating deployments.
Monitoring & Analytics: MLflow dashboards, logs, and performance metrics for AI system insights.

Learn to configure and manage Gen AI environments effectively:

Admin Interface: Navigation through Hugging Face Hub, OpenAI Playground, and AI pipelines.
Automation Tools: MLflow, Kubeflow, and infrastructure-as-code for AI workflows.
Security & Access: Role-based permissions, API key management, ethical AI practices, and compliance.
Performance Tracking: Monitoring accuracy, drift, logs, and generative model utilization.

This module provides hands-on learning for Gen AI application development:

Generative Development: Chatbots, text-to-image models, summarization tools, and automation workflows.
UI & Application Components: Deploying AI-driven dashboards, conversational apps, and multimodal interfaces.
App Building: Designing scalable generative applications, reusable prompts, and automation scripts.
Testing & Deployment: Using staging environments, CI/CD pipelines, and version-controlled AI deployments.

Learn to integrate and automate AI workflows using Gen AI :

MLOps Modules: CI/CD pipelines, infrastructure automation, and configuration management for AI.
Serverless & Automation: Automating model pipelines, orchestration with LangChain, and scheduled triggers.
Integration: Connecting AI with APIs, enterprise systems, and hybrid environments.
Automation Tools Overview: MLflow, Kubeflow, Vertex AI, and Python SDK workflows.

Understand how to track performance and optimize AI operations:

Dashboards & Monitoring: MLflow dashboards, model logs, metrics, and alerting.
Performance Analytics: Accuracy trends, cost optimization, and latency analysis.
KPIs & Metrics: Response time, efficiency, accuracy, scalability, and model drift.
Data Management Tools: Vector databases, ETL pipelines, AI data preprocessing, and generative analytics.

Apply Gen AI skills to real-world AI scenarios:

Project Planning: Define use cases, goals, and timelines for AI implementations.
Live Projects: Work on real Gen AI solutions, conversational apps, and multimodal workflows.
Performance Review: Analyze dashboards, logs, and output quality for improvements.
Capstone Project: Build end-to-end Gen AI solutions including automation, generative applications, and monitoring dashboards.

🎁 Free Addon Programs

Aptitude, Spoken English

🎯 Our Placement Activities

Daily Task, Soft Skills, Projects, Group Discussions, Resume Preparation, Mock Interview

Get Hands-on Experience in Gen AI Projects

Project 1

AI-Powered Resume Screener

Design a system using Python, NLP and Scikit-learn to automatically analyze and score resumes based on job descriptions. This AI tool will help HR departments filter candidates more effectively by identifying relevant skills and experiences.

Project 2

Recommendation System

Develop a recommendation engine using collaborative filtering techniques, TensorFlow and Python. The system will analyze user preferences and behaviors to suggest products, movies or content improving user experience and engagement.

Project 3

Speech-to-Text Conversion Tool

Create a speech recognition system using Python, TensorFlow and SpeechRecognition libraries. This tool will convert spoken language into written text, enabling applications in transcription, voice commands and accessibility features.

Project 4

AI-Powered Fraud Detection System

Build the fraud detection system using machine learning algorithms in Python, like Random Forest or SVM along with Scikit-learn. The system will analyze transaction patterns and flag suspicious activity to prevent financial fraud.

Project 5

AI-Driven Healthcare Diagnosis Tool

Design an AI-based diagnostic tool using Keras and TensorFlow to analyze medical images or patient data. The system will help doctors identify diseases or abnormalities, such as tumors or fractures, improving diagnosis speed and accuracy.

Placement Support Overview

Easy Apply

Internship Highlights for Gen AI

Real-Time Projects

1. Gain hands-on experience by working on live Gen AI applications and automation use cases.
2. Learn real-world problem-solving through chatbot, content generation, and workflow integration scenarios.

Book Session

Skill Development Workshops

1. Participate in focused sessions on trending Gen AI tools and frameworks.
2. Learn directly from industry experts through guided practical exercises on prompts, pipelines, and models.

Book Session

Employee Welfare

1. Enjoy benefits like health coverage, flexible hours, and wellness programs during internships or training.
2. Companies prioritize mental well-being and work-life balance for all employees in Gen AI teams.

Book Session

Mentorship & Peer Learning

1. Learn under experienced Gen AI mentors to guide your technical and career growth.
2. Collaborate with peers to enhance learning through scenario-based exercises and group projects.

Book Session

Soft Skills & Career Readiness

1. Improve communication, teamwork, and time management skills for professional AI roles.
2. Prepare for interviews and workplace dynamics with mock sessions, case studies, and expert guidance.

Book Session

Certification

1. Earn recognized credentials to validate your Gen AI skills.
2. Boost your resume with course or project completion certificates from reputed Gen AI training platforms.

Book Session

Sample Resume for Gen AI (Fresher)

1. Simple and Neat Resume Format
– Use a clean layout with clear sections such as summary, Gen AI skills, certifications, education, and projects.
2. List of AWS Skills You Know
– Highlight skills like Prompt Engineering, LLM Fine-Tuning, LangChain, Vector Databases, Workflow Automation, Model Deployment, Dashboards, and AI-Powered Applications.
3. Real-Time Projects and Achievements
– Include 1–2 Gen AI projects with a brief description, tools used, and key outcomes or achievements.

0 to 1 yr 0 to 2 yrs 0 to 3 yrs Non IT To IT Career Gap to IT Diploma to IT

Company-Specific Interview Questions from Top MNCs

1. What does Generative AI mean and what purpose does it serve?

Ans:

Generative AI refers to subset of artificial intelligence that is designed to create new, original content like text, images, audio and even code, using advanced models such as GPT, BERT or Stable Diffusion. Its primary role is to automate creative processes, generate innovative outputs and enhance business operations by delivering intelligent and relevant solutions. By generating content efficiently, it accelerates tasks that traditionally require human creativity and intervention.

2. What are the key principles for successfully implementing Generative AI?

Ans:

Successful implementation of Generative AI requires a foundation of high-quality data, carefully crafted prompts and the establishment of automated workflows while ensuring proper oversight. Ongoing performance monitoring, adherence to ethical standards and alignment with the company's objectives are essential for maintaining the system’s efficiency, reliability and trustworthiness. These practices help ensure that AI systems deliver consistent and ethical results that meet business goals.

3. What is the best approach to gathering project requirements for Generative AI?

Ans:

Gathering project requirements for Generative AI involves engaging with stakeholders and thoroughly analyzing existing workflows to identify pain points and opportunities for improvement. Techniques such as surveys, interviews and process mapping provide valuable insights that guide decisions on model selection, data preparation and deployment strategies. These efforts ensure that the final AI solution aligns with business needs and addresses the project's core objectives effectively.

4. What factors are important in designing user-friendly Generative AI solutions?

Ans:

Designing user-friendly Generative AI solutions involves focusing on intuitive interfaces, simple prompt inputs and clear output presentations. Including accessibility features, offering practical usage examples and ensuring smooth integration with existing tools significantly enhance the overall user experience. Also, it is essential to create explainable AI outputs so that consumers can readily comprehend and have faith in the outcomes produced by the AI system.

5. What is a Large Language Model (LLM) and how does it function?

Ans:

An advanced neural network called a Large Language Model (LLM) is trained on enormous volumes of text data to comprehend and produce language that is similar to that of humans. In tasks such text summarization, language translation, code generation and conversational AI, these models are frequently employed to produce responses that are context-aware, logical and significant. LLMs enable machines to interact in a natural, human-like manner, making them ideal for applications requiring sophisticated language understanding.

6. How should a monitoring dashboard for Generative AI be designed?

Ans:

A monitoring dashboard for Generative AI should display essential metrics such as accuracy, latency and cost efficiency, using visual elements like graphs and charts to make the data easy to interpret. The dashboard should also track model drift and collect user feedback, ensuring continuous optimization of AI performance. By providing a clear overview of the system’s performance, it supports informed decision-making and enhances the AI system's effectiveness.

7. What does Generative AI Automation entail?

Ans:

Generative AI Automation involves the creation of systems and workflows where AI is responsible for generating content, training models or handling repetitive tasks automatically. This automation boosts the speed, accuracy and consistency of processes, allowing businesses to scale operations, enhance productivity and deploy innovative solutions more efficiently. Automation minimizes human intervention and allows AI to perform tasks with precision and reliability.

8. What are the primary stages involved in a Generative AI project?

Ans:

A Generative AI project typically progresses through several stages: initial requirement analysis, architectural design, model selection and fine-tuning, followed by quality testing. Once these stages are complete, the model is deployed and continuous monitoring is performed to ensure its ongoing accuracy, performance and user satisfaction. These stages ensure that the project meets both technical and business objectives from start to finish.

9. How is the performance of a Generative AI system validated?

Ans:

The performance of a Generative AI system is validated through various metrics, including accuracy, F1 score and real-world testing to assess how well it meets its intended objectives. Feedback from stakeholders is also critical in ensuring the system aligns with business expectations. Additionally, prompt optimization helps refine the model’s output, guaranteeing that the system remains reliable and effective across different scenarios.

10. How can consistency be maintained across multiple Generative AI projects?

Ans:

Consistency across multiple Generative AI projects is maintained by standardizing workflows, prompt structures and documentation practices. Implementing version control, establishing naming conventions and creating reusable templates all contribute to maintaining uniformity across different projects. Regular audits also ensure that processes remain reliable and that each AI solution is developed according to established standards, ensuring quality and consistency.

1. How is Generative AI distinct from traditional AI and what role does it play in modern applications?

Ans:

Using sophisticated models like GPT, DALL·E or Stable Diffusion, generative AI is a subfield of artificial intelligence that focuses on creating creative content, such as writing, graphics, music or code. Unlike traditional AI, which typically focuses on tasks like classification and prediction, Generative AI specializes in creativity, enabling the automatic generation of new and innovative outputs. This capability opens up a world of possibilities, allowing for automation, new creative processes and enhanced solutions across various industries.

2. What skills are crucial for a professional working in Generative AI?

Ans:

A successful professional in Generative AI must have a strong understanding of large language models, multimodal AI systems and frameworks like PyTorch or TensorFlow. Proficiency in prompt engineering, model fine-tuning and pipeline automation is vital for effective model development. Additionally, skills in Python programming, API integration, problem-solving and collaboration are essential for building dependable and scalable AI solutions.

3. What steps should be followed to effectively initiate and execute a new Generative AI project?

Ans:

Launching a Generative AI project begins by clearly defining the objectives, understanding user requirements and identifying challenges through discussions with stakeholders. Once the goals are established, the next step is gathering relevant datasets, fine-tuning models and designing AI workflows. Ongoing testing, feedback loops and continuous optimization throughout the process are key to ensuring successful deployment and reliable results.

4. How would you define a Large Language Model (LLM) and why is it important in the field of Generative AI?

Ans:

A kind of neural network called a significant Language Model (LLM) can comprehend and produce language that is similar to that of humans since it has been trained on a significant amount of text data. These models are widely used in tasks such as text summarization, code generation, translation and conversational AI. LLMs are central to many modern Generative AI applications, as they provide context-aware, coherent outputs that improves user interactions and decision-making.

5. What strategies can be used to design user-friendly and accessible Generative AI solutions?

Ans:

To make Generative AI systems accessible, its important to create intuitive interfaces with clear prompts and consistent outputs. Usability is enhanced for all users by including useful features like tooltips, error-handling, examples and support for accessibility tools like screen readers. These elements ensure that the AI system is easy to interact with regardless of a user’s technical background or experience level.

6. What tools and frameworks are commonly utilized in Generative AI development?

Ans:

Generative AI development often involves tools such as OpenAI APIs and Hugging Face, which provide access to pre-trained models. LangChain is used to design and automate AI workflows, while MLflow and TensorBoard help track model performance and optimization. Additionally, Python, various SDKs and vector databases like Pinecone and Weaviate enable seamless integrations, semantic search and efficient data retrieval.

7. Why are dashboards and monitoring systems crucial in the success of Generative AI projects?

Ans:

For real-time insights into AI model performance, including accuracy, latency and data drift dashboards and monitoring tools are crucial. These tools help teams detect potential issues early, track trends and optimize models as needed. Continuous monitoring ensures that the AI models consistently deliver reliable outputs while remaining aligned with the organization’s goals.

8. How should feedback and issues be addressed during the deployment of Generative AI systems?

Ans:

Handling feedback during AI deployment requires gathering input from both stakeholders and end-users, analyzing gaps and adjusting models or processes accordingly. Addressing issues in a transparent and ethical manner ensures improvements are made to the system which enhances performance while maintaining scalability and reliability. This iterative feedback loop is essential for long-term success and system optimization.

9. What are the best practices for testing Generative AI solutions?

Ans:

Testing Generative AI solutions involves several methods, including user acceptance testing, sandbox testing and regression validation to ensure the models produce stable and accurate results. Continuous feedback sessions and performance evaluations are necessary to address issues and refine the system. This iterative testing approach guarantees that the AI solution functions effectively in real-world scenarios and meets user expectations.

10. How can professionals stay current with developments and trends in Generative AI?

Ans:

To stay updated with Generative AI advancements, professionals should regularly read research papers, attend webinars and engage with communities like OpenAI and Hugging Face. Hands-on experimentation with new tools and frameworks, along with networking with experts in the field, helps maintain up-to-date knowledge and practical skills. Professionals stay competitive and up to date on the newest developments in the rapidly changing AI field thanks to this continuous learning.

1. In what ways does a Generative AI Engineer's role differ from that of a Developer?

Ans:

Generative AI Engineers are responsible for designing and maintaining AI systems, ensuring efficient workflows, pipelines and overall infrastructure. They focus on scaling systems and streamlining automation processes. On the other hand, Developers work on writing code, integrating APIs and fine-tuning models to implement specific features within the AI applications. While Engineers manage the architecture, Developers concentrate on optimizing the individual components and functionality of the AI model.

2. What makes gathering business requirements essential in a Generative AI project?

Ans:

The process of collecting business requirements involves analyzing current workflows, speaking with key stakeholders and aligning with organizational goals. This step ensures that the AI solution meets both the user’s expectations and the company’s objectives, avoiding unnecessary complexity and scope creep. Properly defined requirements guide the project in the right direction, ensuring the solution is impactful and delivers relevant outcomes.

3. What are the key practices for implementing Generative AI solutions successfully?

Ans:

To implement Generative AI solutions effectively, its crucial to utilize high-quality datasets, automate workflows and maintain consistency with naming conventions and organizational standards. Scalable dashboards for tracking performance, coupled with structured testing and continuous validation, contribute to the system’s reliability and efficiency. These practices ensure that the AI models deliver accurate, consistent and high-performing results in real-world applications.

4. What tools and technologies are typically used to develop and deploy Generative AI applications?

Ans:

For developing and deploying Generative AI applications, commonly used tools include OpenAI and Hugging Face APIs, which provide access to pre-trained models. LangChain is helpful in managing automated workflows, while Python and SDKs allow for custom integrations. Additionally, tools like MLflow and TensorBoard are used to track and evaluate performance metrics and vector databases such as Pinecone or Weaviate store embeddings. Scalable deployment is facilitated by technologies like Docker and Kubernetes.

5. How important is data security in Generative AI projects?

Ans:

Data security is crucial in Generative AI projects, as it protects sensitive information throughout training, testing and deployment phases. Security measures such as encryption, secure API management and controlled access prevent unauthorized access or breaches. Strong data security protocols ensure compliance with industry standards, build trust with stakeholders and protect the integrity of the organization’s AI models and data.

6. How can AI pipelines and datasets be efficiently built and managed?

Ans:

Efficiently building AI pipelines starts with clearly defining project goals and the expected outcomes. It involves gathering clean, relevant datasets, processing them for training and fine-tuning models accordingly. Structured workflows, validation checkpoints and ongoing monitoring of data quality are essential to ensure consistent performance and accurate AI outputs throughout the project lifecycle.

7. What are the major phases of executing a Generative AI project?

Ans:

The execution of a Generative AI project begins with an analysis of the project’s requirements and data needs. After that, workflows are designed and models are selected. The project then moves on to data preparation, followed by fine-tuning and constructing AI pipelines. Thorough testing and validation ensure the AI solution is usable and the final deployment involves continuous monitoring to optimize performance.

8. How should stakeholder feedback be incorporated during a Generative AI project?

Ans:

Stakeholder feedback should be managed systematically by documenting their suggestions and prioritizing necessary changes. Based on this feedback, adjustments are made to the models or prompts to improve accuracy and functionality. Transparent communication with stakeholders throughout the process ensures they are kept informed of updates, while iterative testing guarantees the final AI solution meets business objectives and user requirements.

9. What key practices contribute to the success of Generative AI projects?

Ans:

The success of Generative AI projects depends on using reliable pre-trained models, maintaining structured and clean datasets and minimizing hardcoding. Consistent documentation and regular validation are crucial to sustaining accuracy and efficiency over time. Continuous monitoring of models ensures that they remain performant and adaptable as the project evolves, allowing for scalable and maintainable AI solutions.

10. How can professionals remain current in the rapidly advancing field of Generative AI?

Ans:

Staying updated in the field of Generative AI involves regularly reading research papers, AI-related blogs and industry newsletters. Participating in webinars, conferences and community events hosted by organizations like OpenAI and Hugging Face also helps keep knowledge fresh. Additionally, practical experimentation with new tools, technologies and certifications ensures that professionals stay hands-on and competitive as the field continues to evolve.

1. What distinguishes the role of a Generative AI Engineer from that of a Developer?

Ans:

Generative AI Engineers focus on building and maintaining the infrastructure, workflows and automation necessary for AI systems to function efficiently and scale properly. They are responsible for managing datasets and overseeing system architecture. Developers, on the other hand, specialize in writing code, integrating APIs and fine-tuning models to create customized AI applications. While Engineers ensure the operational backbone of the system, Developers work on enhancing its functionality through programming and performance optimization.

2. How are the requirements for a Generative AI project gathered?

Ans:

Gathering requirements for a Generative AI project typically involves engaging stakeholders through interviews, surveys and workshops to understand business objectives and user needs. This process helps define key aspects such as data preparation, model selection and workflow design. Ensuring that the gathered requirements align with organizational goals ensures that the AI solution will deliver meaningful and effective results addressing both technical and business needs.

3. What are the commonly used tools and frameworks for Generative AI development?

Ans:

OpenAI and Hugging Face APIs, which offer access to pre-trained models that may be adjusted for particular applications, are well-liked resources for creating generative AI systems. LangChain is used for automating workflows while Python and SDKs are essential for custom scripting. MLflow and TensorBoard are helpful for tracking performance metrics and vector databases like Pinecone or Weaviate enable efficient semantic search. Dashboards for real-time monitoring support informed decision-making and performance optimization.

4. What is Large Language Model (LLM) and how is it utilized in Generative AI projects?

Ans:

OpenAI and Hugging Face APIs, which offer access to pre-trained models that may be adjusted for particular applications, are well-liked resources for creating generative AI systems. LLMs are used in a variety of applications, including chatbots, text summarization, recommendation engines and code generation. By fine-tuning LLMs and optimizing prompts, these models can produce outputs that align with user requirements and business objectives, ensuring relevance and accuracy in various use cases.

5. How can Generative AI systems maintain security while being user-friendly?

Ans:

Ensuring the security of Generative AI systems involves implementing strict access controls, encrypting sensitive data and managing APIs securely. User-friendliness is achieved by designing intuitive interfaces, providing clear prompts and ensuring consistent understandable outputs. By testing across various platforms and incorporating feedback, the system can be made both secure and accessible, ensuring its easy to navigate for a wide range of users.

6. How are AI dashboards and monitoring systems created for Generative AI projects?

Ans:

Key performance criteria, like accuracy, latency and cost effectiveness, are first identified before developing AI dashboards. Visualization tools like graphs and charts make it easier to interpret these metrics. With continuous feedback and iterative improvements, the dashboard is refined to deliver actionable insights and guide decisions that optimize the system’s performance. This ongoing adjustment ensures the dashboard remains relevant and effective in monitoring AI performance.

7. How are Generative AI solutions validated and tested before they go live?

Ans:

Generative AI solutions undergo various testing phases, including sandbox testing, user acceptance testing and regression checks, to ensure the system's stability and output quality. Feedback from users and stakeholders is incorporated to refine the models and prompts. Continuous monitoring after deployment helps ensure the system maintains accuracy and relevance, guaranteeing that it meets the intended goals and performs reliably in a live environment.

8. What principles guide the successful implementation of Generative AI projects?

Ans:

The successful implementation of Generative AI is built on several core principles, including the use of high-quality, clean datasets and well-documented workflows. Leveraging pre-trained models can accelerate development while maintaining stability through standardized naming conventions and automated processes. These practices help ensure that the AI system remains scalable, accurate and maintainable throughout its lifecycle, making it easier to manage and improve over time.

9. How should conflicting stakeholder feedback be managed?

Ans:

Conflicting feedback should be carefully documented, analyzed and prioritized based on the project’s goals and feasibility. Transparent communication is key in explaining the trade-offs and limitations, ensuring all stakeholders are aligned on decisions. By balancing different viewpoints and focusing on business value, it is possible to make adjustments that address core issues without compromising the performance or quality of the solution.

10. What are the best ways to stay updated on the latest Generative AI trends?

Ans:

Staying up to date with Generative AI involves actively reading research papers, attending webinars and following updates from key AI communities like OpenAI and Hugging Face. Engaging in professional communities, experimenting with new tools and gaining certifications also help maintain practical knowledge and expertise. By combining hands-on experience with continuous learning, professionals can stay current with emerging AI technologies and innovations.

1. What is the difference between a Gen AI Engineer and a Gen AI Developer?

Ans:

A Gen AI Engineer is responsible for building and maintaining the AI infrastructure, including managing data pipelines, automating workflows and ensuring the scalable deployment of models. This role focuses on system architecture and optimizing the AI system for performance and reliability. In contrast, a Gen AI Developer specializes in writing code, integrating APIs and fine-tuning models to create functional AI applications. Developers enhance the system's features and improve model performance through programming and optimization.

2. How are features prioritized when developing a Gen AI solution that addresses multiple requirements?

Ans:

Feature prioritization in a Gen AI solution is based on aligning business goals, user needs and the potential impact on system performance. By evaluating feedback from stakeholders, analyzing process efficiencies and forecasting productivity improvements, the most critical and high-value features are prioritized for implementation. This ensures the solution addresses the most important requirements first, delivering value to both users and the organization.

3. What is the process for designing and deploying a new Gen AI solution?

Ans:

A comprehensive understanding of business goals is the first step in designing and implementing a new Gen AI solution. Next, clean and pertinent datasets are gathered. After selecting the appropriate models, workflows are created, and the system is tested in sandbox environments. Based on feedback from testing, the solution is iteratively refined to ensure it meets the required standards. Once finalized, the solution is deployed for real-time use, with continuous monitoring to ensure it performs as expected.

4. How is security and accessibility maintained in Generative AI systems?

Ans:

Implementing encryption mechanisms, limiting user access and utilizing API authentication techniques to safeguard sensitive data throughout the AI lifecycle are all necessary for ensuring security in generative AI systems. Accessibility is achieved by designing user-friendly interfaces with clear prompts and consistent outputs that work seamlessly across different platforms. These efforts ensure that the system is secure while remaining easy to use for diverse users, regardless of their technical expertise.

5. What metrics are essential for evaluating the success of a Gen AI implementation?

Ans:

Key performance indicators (KPIs) like model correctness, response latency, user engagement and cost efficiency are commonly used to gauge the efficacy of Gen AI solutions. Real-time dashboards and feedback systems provide valuable insights into the system’s performance, enabling ongoing improvements. Monitoring these metrics ensures that the AI solution aligns with business objectives and performs reliably, making adjustments where necessary to enhance the solution's overall impact.

6. Can you describe a challenging Gen AI problem and the approach to solving it?

Ans:

One challenging issue involved dealing with inconsistent outputs from a fine-tuned large language model. To address this, a deep analysis of the dataset was conducted, followed by prompt refinement and the introduction of contextual templates to improve model consistency. Continuous evaluation and optimization were applied to enhance both the accuracy and the user experience, ultimately resolving the issue and improving the system’s overall performance.

7. Which tools and platforms are typically used in the development and management of Gen AI solutions?

Ans:

OpenAI and Hugging Face APIs, which offer access to customizable pre-trained models, are popular tools for creating and overseeing Gen AI solutions. LangChain helps automate the creation of AI workflows, while Python SDKs enable seamless integration of custom features. Performance monitoring is facilitated by tools like MLflow and TensorBoard and vector databases such as Pinecone and Weaviate are used for managing embeddings and enhancing data retrieval processes.

8. How is conflicting stakeholder feedback managed during a Gen AI project?

Ans:

Conflicting stakeholder feedback is carefully analyzed and evaluated based on business priorities and the technical feasibility of proposed solutions. Each concern is documented and communicated transparently to ensure all parties understand the rationale behind decisions. Data-driven insights are used to make informed trade-offs, ensuring the final solution aligns with both stakeholder expectations and project capabilities while maintaining project goals and quality.

9. Why is data critical in Generative AI projects?

Ans:

Data is the foundation of any successful Gen AI project, as it directly influences model training, inference and the quality of the output generated. High-quality, clean and relevant datasets are essential for improving model performance. Ongoing monitoring of data ensures the AI system's reliability, ethical integrity and consistency. Proper data management practices lead to actionable, trustworthy AI outputs that meet business objectives.

10. How do professionals continue to grow in the field of Generative AI?

Ans:

It takes ongoing education through research articles, webinars and involvement in AI groups to stay on the cutting edge of the field of generative AI. Professionals can expand their expertise by experimenting with new frameworks, tools and models and by pursuing relevant certifications. This commitment to hands-on learning, combined with engagement in emerging AI trends, ensures professionals remain adaptable and knowledgeable in the fast-evolving AI landscape.

Disclaimer Note:

The details mentioned here are for supportive purposes only. There are no tie-ups or links with the corresponding PGs.

Ladies PG Accommodation

Velachery

Sanz Live Women's PG : 72007 19990

Krishnaveni Castle : 90801 95007

Anna Nagar

Pentos Women's PG : 93427 57797

Women's Nest - Ladies PG : 94451 25894

OMR

MSR Luxury PG : 89399 91922

Porur

Sree Shakthi Ladies Hostel : 90031 98767

Friends Ladies Hostel : 73389 19836

Tambaram

CK HIVE Girls Ladies PG90948 58303

Layaa Women’s Hostel63797 00573

Mens PG Accommodation

Velachery

Stayflix PG for Men's : 96000 45088

DJ Men's PG Velachery : 90803 19242

Anna Nagar

Stay Inn Men's Hostel(PG) : 94454 87884

Brights Boys Hostel : 88387 88921

OMR

Sree Siddhi Vinayaka Mens PG : 95577 95579

Rudhra Men’s PG : 93636 45199

Porur

SIDDHANS Men's PG : 88259 71908

Jaswanth Raaj Mens PG : 89255 15888

Tambaram

Sri Sai Grn Men's PG : 81228 24076

Rolexx Men's PG : 80988 84848

Top Gen AI Job Opportunities for Freshers

1. Gen AI Roles at Startups and Tech Companies
2. Campus Placements and AI Research Jobs
3. Internship-to-Job Programs
4. Apply Through Job Portals
5. Skills That Help You Get Hired

Gen AI LinkedIn Jobs Gen AI Naukri Jobs Gen AI Indeed Jobs Gen AI Quikr Jobs Gen AI Shine Jobs Gen AI Monster Jobs Gen AI Glassdoor Jobs Gen AI Freshersworld Jobs

Getting Started With Gen AI Course in Anna Nagar

Easy Coding

8 Lakhs+ CTC

No Work Pressure

WFH Jobs (Remote)

Why Gen AI is the Ultimate Career Choice

High Demand

Companies prefer multi-skilled professionals can handle entire project cycles.

Global Opportunities

Open doors to remote and international job markets.

High Salary

Enjoy competitive salaries and rapid career advancement.

Flexible Career Path

Explore roles such as developer, architect, freelancer, or entrepreneur.

Future-Proof Career

Stay relevant with skills that are consistently in demand in the evolving tech landscape.

Versatility Across Industries

Work in various domains like e-commerce, healthcare, finance, and more.

Career Support

Placement Assistance

Exclusive access to ACTE Job portal

Mock Interview Preparation

1 on 1 Career Mentoring Sessions

Career Oriented Sessions

Resume & LinkedIn Profile Building

Get Advanced Gen AI Certification

You'll receive a certificate proving your industry readiness.Just complete your projects and pass the pre-placement assessment.This certification validates your skills and prepares you for real-world roles.

📜 Industry-Recognized Certification

Earn official, shareable certifications recognized by top companies — a powerful proof of your job-ready skills.

🚀 Get Discovered by Top Employers

Let recruiters come to you with your verified profile — focus on learning while we connect you to real opportunities.

💼 Real-World Project Experience

Work on real-world projects to sharpen your skills and build a portfolio that impresses employers instantly.

What are the different types of certifications available for Gen AI training?

The Gen AI training program offers a range of certification options to cater to various skill levels and career aspirations, including:

Certified Generative AI Practitioner (CGAP)
Generative AI Developer Certification
Generative AI Engineer Certification
Generative AI Specialist in Natural Language Processing (NLP)
Professional Generative AI Architect Certification

Does earning a Gen AI certification guarantee a job?

Although earning a Generative AI certification demonstrates expertise in automation, ML and artificial intelligence, it significantly enhances employment prospects. A certification not only boosts credibility but also opens doors to exciting career opportunities. While practical experience, problem-solving skills and communication abilities further strengthen a candidate’s profile, the certification itself acts as a valuable asset that employers highly regard.

How long does it usually take to finish a Gen AI certification program?

The time needed to complete a Gen AI certification depends on the specific program and the learner's pace. For example, entry-level certifications like the Gen AI Practitioner can typically be completed within 6 to 8 weeks. More advanced certifications such as the Gen AI Developer or Professional Architect may take around 3 to 6 months. Intensive bootcamps tend to last 8 to 12 weeks, while self-paced learners can take longer or progress faster, depending on their individual schedule and availability.

What are the primary advantages of obtaining a Gen AI certification?

Earning a Gen AI certification offers several key benefits, including:

Gaining global recognition for expertise in AI and machine learning.
Improving career opportunities and increasing earning potential.
Boosting professional credibility in the rapidly growing tech industry.
Enhancing visibility on resumes, LinkedIn profiles and professional networks.
Gaining hands-on experience through practical projects and real-world applications.

What are the best strategies to prepare for a Gen AI certification exam?

To effectively prepare for a Gen AI certification exam, consider the following approaches:

Participate in structured programs with mentorship for focused learning.
Practice consistently using AI platforms, APIs and live project exercises to gain hands-on experience.
Review official study materials and take mock exams to assess your readiness.
Apply knowledge by working on real-world AI projects and gaining practical experience.
Join AI communities and online forums to stay updated on the latest trends, gather insights and receive peer support.

Complete Your Course

A Downloadable Certificate in PDF Format, Immediately Available to You When You Complete Your Course.

Get Certified

A Physical Version of Your Officially Branded and Security-Marked Certificate.

Get Certified

Lowest Gen AI Training Fees in Anna Nagar

Affordable, Quality Training for Freshers to Launch IT Careers & Land Top Placements.

Call Course Advisor

How is ACTE's Gen AI Training Different?

Feature

ACTE Technologies

Other Institutes

Affordable Fees

Competitive Pricing With Flexible Payment Options.

Higher Gen AI Fees With Limited Payment Options.

Industry Experts

Well Experienced Trainer From a Relevant Field With Practical AWS Training

Theoretical Class With Limited Practical

Updated Syllabus

Updated and Industry-relevant Gen AI Course Curriculum With Hands-on Learning.

Outdated Curriculum With Limited Practical Training.

Hands-on projects

Real-world Gen AI Projects With Live Case Studies and Collaboration With Companies.

Basic Projects With Limited Real-world Application.

Certification

Industry-recognized Gen AI Certifications With Global Validity.

Basic Gen AI Certifications With Limited Recognition.

Placement Support

Strong Placement Support With Tie-ups With Top Companies and Mock Interviews.

Basic Placement Support

Industry Partnerships

Strong Ties With Top Tech Companies for Internships and Placements

No Partnerships, Limited Opportunities

Batch Size

Small Batch Sizes for Personalized Attention.

Large Batch Sizes With Limited Individual Focus.

LMS Features

Lifetime Access Course video Materials in LMS, Online Interview Practice, upload resumes in Placement Portal.

No LMS Features or Perks.

Training Support

Dedicated Mentors, 24/7 Doubt Resolution, and Personalized Guidance.

Limited Mentor Support and No After-hours Assistance.

Gen AI Course FAQs

1. What are the basic prerequisites for starting a career in Generative AI?

A foundation in basic computer skills, logical thinking and analytical abilities is essential for entering the field of Generative AI. Having a genuine interest in artificial intelligence, a curiosity to explore new tools and a problem-solving mindset are highly beneficial. While prior programming experience can be helpful, it is not a requirement, as most introductory courses begin with fundamental concepts.

2. What is the current demand for Generative AI professionals in various industries?

The demand for professionals in Generative AI is experiencing rapid growth across numerous sectors, including IT, healthcare, finance and retail. Many organizations are integrating AI-powered automation and intelligent analytics to boost efficiency, streamline operations and enhance decision-making processes. This trend is creating a wide range of opportunities for roles such as AI developers, engineers and specialists in Generative AI, globally.

3. What are the core technologies covered in the Generative AI training program?

The training curriculum encompasses a wide variety of crucial topics that form the backbone of Generative AI development. It includes foundational AI principles, model fine-tuning, natural language processing (NLP), computer vision and workflow automation using AI APIs. Additionally, the program covers performance monitoring, data analytics and optimization techniques, make sure that AI systems run efficiently and can scale effectively.

4. Do the Gen AI training programs include real-time project work?

Yes, the curriculum is designed with a hands-on approach, where learners engage in practical projects. These tasks typically involve building AI models, creating chatbots, automating content generation and designing workflow automation tools. Such real-time projects are essential for developing problem-solving skills and providing learners with direct experience in real-world AI applications.

5. Does the course provide guidance on resume building and job readiness?

Yes, career support is a critical part of the program. Learners receive guidance on how to present their AI skills, certifications and project experience effectively. The training includes mock interviews, career counseling and portfolio development sessions to enhance employability and prepare graduates for securing positions in top AI firms.

1. Who is eligible to enroll in the Generative AI training program?

The program is open to a wide range of individuals, including students, recent graduates, IT professionals and even those with no technical background. As the training begins with basic AI and machine learning concepts, no prior technical experience is required, making it accessible to beginners.

2. Is a formal degree required to pursue a career in Generative AI?

A formal degree is not a prerequisite for entering the field of Generative AI. Employers tend to prioritize hands-on experience, practical skills and certifications over formal education. Many individuals successfully transition into AI roles after completing structured training and demonstrating their capabilities through applied AI projects.

3. What skills are helpful to have before starting the Generative AI training program?

At the beginner level, basic computer skills and logical thinking are sufficient to start. Although programming experience can be advantageous for tackling more advanced AI tasks, it is not a necessity for entry-level roles. An inquisitive mindset towards automation, emerging technologies and data-driven solutions will greatly accelerate the learning process.

4. Is prior coding knowledge necessary for enrolling in the Generative AI program?

No prior coding experience is required to begin the program. The course leverages no-code and low-code tools, enabling students to build AI applications without needing deep programming skills. As learners progress, coding concepts are introduced to handle tasks like model customization and process automation.

1. What kind of placement assistance is provided upon completion of the Generative AI training?

Placement assistance is a core component of the program, offering comprehensive support such as resume development, mock interviews and career mentoring. Training providers often collaborate with AI-focused companies, startups and tech organizations, helping graduates find roles that match their Gen AI skill sets.

2. Will learners gain experience working on live projects to enhance their resumes?

Yes, learners work on live projects that provide valuable, hands-on experience. These projects may include building AI models, developing chatbots, automating workflows and generating content which significantly boosts the credibility of a professional portfolio and strengthens technical knowledge.

3. Are graduates eligible to apply for jobs in top AI and IT companies after completing the training?

Completing the Gen AI program equips learners with the skills needed for roles in prestigious companies such as TCS, Infosys, Wipro, Accenture and Capgemini. Graduates are also well-prepared to pursue opportunities in top global AI firms that seek certified professionals with strong technical and problem-solving abilities.

4. Is placement support available for beginners with no prior work experience?

Yes, the program offers tailored support for beginners, including job readiness training, mock assessments and hands-on projects. Even individuals with no prior IT experience are guided through the transition into AI roles with continued assistance and practical exposure.

1. Will learners receive a certificate upon completing the Gen AI course?

Yes, participants receive a recognized certificate upon successful completion of the course. This certification validates proficiency in key AI tools, workflows and practical projects. Additionally, learners are guided to pursue advanced certifications such as AI Developer or AI Engineer to further enhance their credentials.

2. Is investing in Gen AI training a worthwhile decision for career advancement?

Generative AI is one of the fastest-growing technological fields globally, offering a wealth of career opportunities. With certification, professionals can explore high-demand roles in sectors like fintech, healthcare and IT. Certified individuals often secure higher-paying positions and experience long-term career growth in the AI industry.

3. Are there any specific prerequisites to enroll in the Gen AI certification course?

There are no strict prerequisites for enrolling in the Gen AI certification course. Basic computer literacy and logical reasoning are enough to start. The course progressively advances students' understanding of AI from fundamental ideas to sophisticated abilities, catering to both novices and experts.

4. How does certification benefit long-term career development?

A Gen AI certification enhances technical expertise and opens up opportunities for roles such as AI Developer, NLP Specialist and Automation Engineer. It not only improves employability and earning potential but also prepares professionals for advanced roles in the rapidly evolving AI sector.

5. What skills are developed during Gen AI certification training?

Through the Gen AI certification program, learners gain hands-on experience in building AI models, engineering prompts and automating processes. The course also covers NLP, computer vision and AI analytics, empowering students to design, implement and optimize intelligent AI systems for various applications.

1. Does the program include placement assistance after course completion?

Yes, full placement assistance is provided, including resume reviews, mock interviews and job referrals. Institutes often partner with hiring companies in the AI and automation fields to ensure graduates have access to real job opportunities.

2. Why do training fees vary between different institutes?

The fees for Gen AI training can differ due to factors such as course content depth, teaching methods, availability of learning resources and additional support services. Institutes offering practical, hands-on training with modern tools and well-structured programs typically charge higher fees compared to standard courses.

3. Is the Gen AI course affordable for beginners?

Yes, both professionals and students will find the course affordable and easily accessible. Many institutes offer flexible payment options, discounts and scholarships making it an excellent investment given the career benefits and growth opportunities in AI.

4. Are the fees the same in all cities for the Gen AI course?

Yes, the fees for the Gen AI program are consistent across various cities and training modes (classroom, online or fast-track). This ensures that all participants receive the same quality of education and value regardless of their location.

Find Gen AI Training in Other Cities

Learn (Generative AI, Prompt Engineering, LLMs, MLOps, NLP, Computer Vision, and AI-Powered Applications) at just 22,000/- Only.