Industry Skills Classroom Training

Gen AI Engineer

This course enable you to walk out not just with knowledge — but with the confidence, skills, and real project experience to land a Gen AI Engineer role. You will be fully prepared to build LLM applications, generate images with diffusion models, process speech with Whisper, work with multimodal AI, fine tune foundation models, and deploy production-ready Gen AI systems — ready to perform from Day 1 on the job.

5.0 (3 ratings)
9 Total Learners 6 months | 4 hours/day Professional Tamil

What You'll Learn

  • Master Python, PyTorch & Transformer Architecture for Gen AI
  • Build Intelligent LLM Applications with LangChain & RAG
  • Generate Images with Stable Diffusion & Diffusion Models
  • Process Speech & Audio with Whisper & Voice AI Pipelines
  • Fine Tune Foundation Models with LoRA, QLoRA & PEFT
  • Go from Job Seeker to Job-Ready Gen AI Engineer

Requirements

  • Basic Computer Knowledge
  • No need of previous coding knowledge
  • Suitable for both IT and Non-IT students

Tools & Technologies

Python Python
PyTorch PyTorch
Ollama Ollama
Hugging Face Hugging Face
Langchain Langchain
LlamaIndex LlamaIndex
ChromaDB ChromaDB
Stable Diffusion Stable Diffusion
ControlNet ControlNet
Whisper Whisper
LLaVA LLaVA
Streamlit Streamlit
vLLM vLLM
Fast API Fast API
Phoenix Phoenix
MLflow MLflow
Docker Docker
GitHub GitHub
Google Colab Google Colab

Course Description

A professionally designed, industry-aligned classroom program built exclusively for job seekers who are serious about becoming a Generative AI Engineer. This is the most comprehensive Gen AI course available — training you across text, image, audio, and multimodal AI — from building intelligent LLM applications and RAG systems to generating images with diffusion models and processing speech with Whisper — giving you the confidence to walk into any AI company and deliver from your very first day at work.

Right for You?

Who This Course is For

  • Fresh Graduates Looking to Start Their Career in Generative AI
  • Job Seekers Who Want to Master the Full Spectrum of Gen AI — Text, Image, Audio & Multimodal
  • Candidates Who Prefer Structured Classroom Learning with Mentor Support
  • Anyone Ready to Commit and Become a Job-Ready Gen AI Engineer

Course Curriculum

21 modules • 207 lessons
Module 1: Python for Gen AI Engineer 13 lessons
  • Introduction to Python & Environment Setup
  • Python Syntax & Data Types
  • Variables, Operators & Expressions
  • Conditional Statements & Loops
  • Functions & Modules
  • Lists, Tuples, Sets & Dictionaries
  • File Handling & Exception Handling
  • Object Oriented Programming
  • Working with JSON & YAML Files
  • Virtual Environments & Package Management
  • Async Programming & Concurrency
  • Python Interview Preparation
  • Module 1 Clearance Test
Module 2: NLP Fundamentals 10 lessons
  • Introduction to Natural Language Processing
  • Text Preprocessing & Cleaning
  • Tokenization - Word, Sentence & Subword
  • Stemming & Lemmatization
  • Bag of Words & TF-IDF
  • Word Embeddings - Word2Vec & GloVe
  • Sentence Embeddings & Semantic Similarity
  • Text Classification & Sentiment Analysis
  • Named Entity Recognition (NER)
  • Module 2 Clearance Test
Module 3: Deep Learning & Transformer Architecture 12 lessons
  • Introduction to Deep Learning for Gen AI
  • Neural Networks - Perceptron & Multilayer Networks
  • Activation Functions, Loss Functions & Optimizers
  • Backpropagation & Gradient Descent
  • Introduction to PyTorch
  • PyTorch Tensors & Operations
  • Building Neural Networks with PyTorch
  • Attention Mechanism - Self Attention & Multi-Head Attention
  • Transformer Architecture - Encoder & Decoder
  • Positional Encoding & Layer Normalization
  • BERT, GPT & T5 Architecture Deep Dive
  • Module 3 Clearance Test
Module 4: Large Language Models - Foundation to Advanced 12 lessons
  • Introduction to Large Language Models
  • LLM Pre-training - Concepts & Data Requirements
  • Instruction Tuning & RLHF Concepts
  • Context Windows, Temperature & Sampling Strategies
  • Open Source LLM Landscape - Llama, Mistral, Phi & Gemma
  • Setting Up & Running Models with Ollama
  • Loading & Running Models from Hugging Face
  • Model Quantization - GGUF, GPTQ & AWQ
  • Running Quantized Models Efficiently
  • Choosing the Right LLM for Production Use Cases
  • Hallucination, Bias & LLM Limitations
  • Module 4 Clearance Test
Module 5: Prompt Engineering & Advanced Techniques 12 lessons
  • Introduction to Prompt Engineering
  • Zero Shot & Few Shot Prompting
  • System Prompts & Role-Based Prompting
  • Chain of Thought Prompting
  • Tree of Thought Prompting
  • Structured Output Control - JSON & XML Outputs
  • Context Window Management Strategies
  • Handling Hallucinations with Prompt Design
  • Prompt Templates & Reusability
  • Prompt Injection & Security Awareness
  • Advanced Prompt Optimization Techniques
  • Module 5 Clearance Test
Module 6: Working with LLM APIs & Local Models 9 lessons
  • Introduction to LLM APIs & Local Model Serving
  • Running LLMs Locally with Ollama API
  • Streaming Responses from Local LLMs
  • Using Hugging Face Inference API
  • Using Open Source Models via Hugging Face Pipelines
  • Function Calling with Open Source Models
  • Managing Model Parameters & Token Usage
  • Switching Between Different LLM Providers
  • Module 6 Clearance Test
Module 7: LangChain for Gen AI Applications 11 lessons
  • Introduction to LangChain & Its Architecture
  • LangChain Components - Models, Prompts & Chains
  • Integrating Ollama & Hugging Face with LangChain
  • Building Simple Chains with LangChain
  • LangChain Expression Language (LCEL)
  • Memory & Conversation History in LangChain
  • LangChain Document Loaders & Text Splitters
  • LangChain Retrievers & Vector Store Integration
  • LangChain Tools & Tool Binding
  • LangChain Output Parsers & Structured Responses
  • Module 7 Clearance Test
Module 8: Vector Databases & Embeddings 10 lessons
  • Introduction to Vector Databases & Why They Matter
  • Understanding Embeddings & Vector Representations
  • Embedding Models from Hugging Face & Ollama
  • Introduction to ChromaDB
  • Setting Up & Working with ChromaDB
  • Introduction to FAISS
  • Similarity Search & Semantic Search with FAISS
  • Indexing & Querying Vector Stores
  • Metadata Filtering in Vector Databases
  • Module 8 Clearance Test
Module 9: RAG - Retrieval Augmented Generation 10 lessons
  • Introduction to RAG & Why It Solves Hallucination
  • RAG Architecture - Indexing, Retrieval & Generation
  • Building a Basic RAG Pipeline with Ollama & ChromaDB
  • Document Loading & Chunking Strategies
  • Advanced Retrieval Techniques - Hybrid Search & Reranking
  • Introduction to LlamaIndex
  • Building RAG Pipelines with LlamaIndex
  • Building a Document Q&A Application
  • RAG Evaluation & Performance Tuning
  • Module 9 Clearance Test
Module 10: LLM Agents & Tool Use 10 lessons
  • Introduction to LLM Agents & Agentic AI
  • ReAct Framework - Reasoning & Acting
  • Building Simple Agents with LangChain & Ollama
  • Function Calling & Tool Integration
  • Building Custom Tools for Agents
  • Agent Memory & State Management
  • Multi-Agent Systems - Concepts & Use Cases
  • Building a Multi-Agent Pipeline
  • Handling Agent Errors & Fallbacks
  • Module 10 Clearance Test
Module 11: Dataset Preparation for Fine Tuning 10 lessons
  • Introduction to Fine Tuning & When to Use It
  • Types of Fine Tuning - Instruction, Task & Domain Specific
  • Dataset Formats - Alpaca, ShareGPT & Chat Format
  • Collecting & Curating Training Data
  • Data Cleaning & Quality Filtering for LLMs
  • Data Augmentation for LLM Training
  • Tokenizing & Formatting Datasets with Hugging Face
  • Splitting & Validating Fine Tuning Datasets
  • Pushing Datasets to Hugging Face Hub
  • Module 11 Clearance Test
Module 12: Fine Tuning Foundation Models - LoRA & PEFT 12 lessons
  • Introduction to Parameter Efficient Fine Tuning (PEFT)
  • LoRA - Low Rank Adaptation Concepts
  • QLoRA - Quantized LoRA for Resource Efficient Fine Tuning
  • Setting Up Google Colab with T4 GPU for Fine Tuning
  • Setting Up Fine Tuning Environment with Hugging Face & PEFT
  • Fine Tuning Llama & Mistral with LoRA
  • Fine Tuning with TRL - Supervised Fine Tuning Trainer
  • Instruction Fine Tuning on Custom Datasets
  • Merging LoRA Adapters with Base Model
  • Saving & Loading Fine Tuned Models
  • Pushing Fine Tuned Models to Hugging Face Hub
  • Module 12 Clearance Test
Module 13: Image Generation & Diffusion Models 11 lessons
  • Introduction to Generative AI for Images
  • How Diffusion Models Work - Concepts & Architecture
  • Introduction to Stable Diffusion & SDXL
  • Setting Up Diffusers Library by Hugging Face
  • Text-to-Image Generation with Stable Diffusion
  • Image-to-Image Generation & Inpainting
  • ControlNet - Guided Image Generation
  • Prompt Engineering for Image Generation
  • Fine Tuning Diffusion Models - Concepts
  • Building an Image Generation Application
  • Module 13 Clearance Test
Module 14: Audio & Speech AI 10 lessons
  • Introduction to Audio AI & Speech Processing
  • Audio Data Fundamentals - Waveforms, Spectrograms & Features
  • Introduction to Whisper by OpenAI
  • Setting Up & Running Whisper Locally
  • Speech-to-Text Transcription with Whisper
  • Multilingual Speech Recognition with Whisper
  • Introduction to Text-to-Speech - Concepts & Industry Options
  • Integrating Speech AI into Gen AI Pipelines
  • Building a Voice-Enabled AI Application
  • Module 14 Clearance Test
Module 15: Multimodal AI 10 lessons
  • Introduction to Multimodal AI & Use Cases
  • How Vision Language Models Work
  • Introduction to LLaVA - Vision Language Model
  • Setting Up & Running LLaVA with Ollama
  • Image Understanding & Visual Question Answering
  • Building Text + Image Pipelines
  • Document Understanding with Multimodal Models
  • Combining LLM + Image + Speech in One Pipeline
  • Real-World Multimodal Use Cases
  • Module 15 Clearance Test
Module 16: Gen AI Application Development 8 lessons
  • Introduction to Gen AI App Development
  • Building a Chat Interface with Streamlit & Ollama
  • Building a Document Q&A App with RAG & Streamlit
  • Building an Image Generation App with Streamlit
  • Building a Voice-Enabled AI Assistant
  • Building a Multimodal AI Application
  • Deploying Gen AI Apps with Streamlit
  • Module 16 Clearance Test
Module 17: Model Serving & Optimization 9 lessons
  • Introduction to Model Serving for Production
  • Model Quantization for Efficient Serving
  • Introduction to vLLM for High Performance Serving
  • Setting Up & Running Models with vLLM
  • Batching & Throughput Optimization with vLLM
  • Building a REST API for Gen AI with FastAPI
  • Streaming Gen AI Responses via FastAPI
  • Containerizing Gen AI APIs with Docker
  • Module 17 Clearance Test
Module 18: LLM Evaluation & Safety 9 lessons
  • Introduction to Gen AI Evaluation
  • Evaluation Metrics - BLEU, ROUGE & BERTScore
  • RAG Evaluation - Faithfulness, Relevance & Completeness
  • Image Generation Evaluation - FID & CLIP Score
  • Hallucination Detection & Mitigation
  • Bias & Fairness in Gen AI Outputs
  • Content Moderation & Output Filtering
  • Responsible AI Practices for Gen AI Engineers
  • Module 18 Clearance Test
Module 19: LLM Observability & Monitoring 9 lessons
  • Introduction to Gen AI Observability
  • Tracking Token Usage & Model Performance
  • Monitoring Latency & Response Quality
  • Introduction to Phoenix by Arize
  • Tracing & Debugging Gen AI Pipelines with Phoenix
  • Introduction to MLflow for Gen AI Experiment Tracking
  • Logging & Comparing Gen AI Experiments with MLflow
  • Production Monitoring Best Practices for Gen AI Apps
  • Module 19 Clearance Test
Module 20: Version Control & Gen AI Project Structure 8 lessons
  • Introduction to Git & GitHub
  • Git Basics - Clone, Commit, Push & Pull
  • Branching, Merging & Pull Requests
  • Gen AI Project Folder Structure Best Practices
  • Managing API Keys & Secrets Safely with .env
  • Writing README & Documenting Gen AI Projects
  • Building a GitHub Portfolio for Gen AI Engineering
  • Module 20 Clearance Test
Module 21: Projects 2 lessons
  • Mentored Project
  • Capstone Project
Our Advantage

Why Choose This Course

  • Built Exclusively for Job Seekers
  • Only Course Covering All 4 Modalities (Text, Image, Audio and Multimodal)
  • Classroom Training with Real Mentor Guidance
  • Build a Portfolio That Makes You Unstoppable

Your Instructor

Parthiban Kannan

Parthiban Kannan

Co-founder & Manager

24 Courses

14+ years of experience in digital transformation, leading teams of 15+ members and delivering 160+ projects for organizations like Lakshmi Machine Works, Milacron, Schneider Electric, Aatomz Research, and Variablz Technologies. AI innovator with a patent approved by the Government of India, with strong expertise in Data Science, Data Analysis, Business Intelligence, and Software Development.

Course Outcomes

What Our Students Build

Articles, projects, research papers and more — real work by our learners applied beyond the classroom.

View All Outcomes
FAQ

Frequently Asked Questions

Is this course suitable for freshers with no AI experience?

Yes. This course starts from Python and NLP basics and gradually takes you through the complete Generative AI stack u2014 no prior AI experience is required to join.

What are the eligibility criteria to join this course?

Any graduate from any degree is eligible, provided their degree includes Mathematics as a subject.

Is this course suitable for college students or working professionals?

No. This course is exclusively designed for job seekers who are seriously looking to start their career as a Gen AI Engineer.

Is this an online or offline course?

This is a completely classroom-based training program conducted at our Cuddalore location.

Will I work on real projects during this course?

Yes. You will work on a Mentored Project with trainer guidance and an independent Capstone Project u2014 both built around real-world Generative AI scenarios covering multiple modalities.

What is the difference between this course and the LLM Developer or LLM Engineer course?

The LLM Developer course focuses on building text-based LLM applications. The LLM Engineer course adds fine tuning and model optimization. The Gen AI Engineer course covers everything in both courses plus Image Generation, Speech AI, and Multimodal AI u2014 making it the most comprehensive AI course we offer.

Do I need a GPU for fine tuning during this course?

No personal GPU is required. For all fine tuning modules, we use Google Colab's free T4 GPU u2014 completely free, no setup cost, no hardware purchase needed.

Will I learn image generation in this course?

Yes. Module 13 is fully dedicated to Image Generation covering Stable Diffusion, SDXL, ControlNet, and building real image generation applications using the Diffusers library by Hugging Face.

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