AI & Generative AI Track

Become a Job-Ready AI Engineer

Master AI & Generative AI Program

A 20-week, mentor-led AI engineering program that takes you from Python basics to building production-grade GenAI applications, RAG pipelines and autonomous AI agents. No prior AI experience needed — just curiosity and commitment.

Live Instructor-LedPython for AILangChain & LlamaIndexRAG & Vector DBsAI AgentsLMS AccessPlacement Support

20 Weeks

4 Phases

12+ Projects

Live Mentorship

Learning Path

Python & AI Foundations

Prompt Engineering & LLMs

Generative AI & RAG

AI Agents & Deployment

Full Syllabus

Complete Curriculum

Click any module to expand topics. Every phase ends with a real-world project.

Phase 1

Python & AI Foundations

4 Weeks

Build the Python and data science foundations required for AI engineering. Learn how to work with data, train basic ML models, and understand how AI pipelines are structured before touching LLMs.

Week 1Python for AI Engineering

8 topics

Topics Covered

Python syntax: variables, lists, dicts, sets, tuples
Functions, closures, decorators and generators
NumPy: arrays, broadcasting, vectorised operations
Pandas: DataFrames, filtering, groupby, merge, pivot
Matplotlib & Seaborn: charts, subplots, heatmaps
File I/O: CSV, JSON, parquet, API responses
Virtual environments and pip dependency management
Jupyter notebooks and Google Colab setup

Tools Used

Python 3.11NumPyPandasMatplotlibJupyterVS Code

Hands-On Lab

Build a data analysis pipeline processing 50,000 rows of real e-commerce data — clean, enrich, visualise and export insights

✓ Outcome: A reproducible Jupyter notebook with 10 business insights and charts ready for a data science portfolio

Week 2Machine Learning Foundations

8 topics

Topics Covered

Supervised vs unsupervised vs reinforcement learning
Regression: linear, polynomial, ridge, lasso
Classification: logistic regression, decision trees, random forest
Feature engineering: encoding, scaling, selection
Train/test split, cross-validation, hyperparameter tuning
scikit-learn Pipelines: preprocessing + model in one object
Model evaluation: accuracy, F1, ROC-AUC, confusion matrix
Overfitting, underfitting, bias-variance tradeoff

Tools Used

scikit-learnXGBoostPandasMatplotlib

Hands-On Lab

Build and evaluate a customer churn prediction model — feature engineering, model selection, tuning and a web demo

✓ Outcome: Deployed churn model achieving >85% accuracy with explainability via SHAP values

Week 3–4Neural Networks & Deep Learning

9 topics

Topics Covered

Perceptrons, layers, activation functions (ReLU, sigmoid, softmax, GELU)
Forward propagation and backpropagation (chain rule intuition)
Loss functions: MSE, cross-entropy, BCE
Optimisers: SGD, Adam, AdamW — learning rate schedulers
Batch normalisation, dropout, weight decay
TensorFlow/Keras: Sequential and Functional API
CNNs: convolutional layers, pooling, ResNet architecture
Transfer learning: fine-tune ImageNet models
GPU training setup: CUDA, Google Colab T4/A100

Tools Used

TensorFlowKerasPyTorchGoogle ColabWeights & Biases

Hands-On Lab

Train an image classification model on a real dataset — fine-tune ResNet50 to 92%+ accuracy and deploy as a REST API

✓ Outcome: A served image classifier with <200ms inference time, documented model card, and W&B experiment tracking

Phase 1 Capstone — AI Data Pipeline

Automated data ingestion + cleaning pipeline
EDA notebook with 15+ visualisations
Trained ML classification model (>85% accuracy)
Model served as FastAPI endpoint
GitHub repo with README and Docker setup

Phase 2

Prompt Engineering & LLMs

4 Weeks

Master the art and science of working with Large Language Models. Learn how to engineer prompts, consume LLM APIs, and build AI-powered applications using OpenAI, Claude, and Azure OpenAI — skills hiring for in 2025.

Week 5LLM Architecture & API Consumption

8 topics

Topics Covered

Transformer architecture: attention, self-attention, multi-head attention (conceptual)
GPT-5.5, Claude Opus 4.8, Gemini 3 Pro, DeepSeek V4 Pro, Llama 4 — capability comparison (May 2026)
OpenAI API: chat completions, streaming, vision, function calling
Azure OpenAI Service: deployment, API version management
Token economics: counting tokens, context windows, cost optimisation
Temperature, top-p, frequency/presence penalty — when to tune each
JSON mode, structured outputs and response_format
Rate limiting, retry logic and error handling in production

Tools Used

OpenAI APIAzure OpenAIPython openai SDKtiktoken

Hands-On Lab

Build an AI Q&A bot with streaming responses, token cost tracking and automatic retry on rate limits

✓ Outcome: Production-quality LLM wrapper with cost dashboard showing spend per session

Week 6Prompt Engineering Techniques

8 topics

Topics Covered

Zero-shot, one-shot and few-shot prompting
Chain-of-thought (CoT): standard and zero-shot CoT
Tree-of-Thought (ToT) for multi-step reasoning
ReAct prompting: Reasoning + Acting pattern
Role prompting and system message design for consistent personas
Structured output generation: JSON, XML, markdown tables
Prompt templating with Jinja2 and f-strings
Prompt versioning, evaluation and A/B testing strategies

Tools Used

OpenAI PlaygroundLangSmithPromptLayer

Hands-On Lab

Engineer a prompt system for a document summarisation service — measure quality with ROUGE scores and human eval

✓ Outcome: A/B tested prompt system with documented evaluation showing 40% quality improvement over baseline

Week 7–8LangChain Fundamentals

8 topics

Topics Covered

LangChain architecture: Chains, Runnables (LCEL), Components
PromptTemplate, ChatPromptTemplate, MessagesPlaceholder
Output parsers: PydanticOutputParser, StructuredOutputParser
Document loaders: PDF, CSV, web, Notion, Google Drive
Text splitters: RecursiveCharacterTextSplitter, semantic chunking
Memory: ConversationBufferMemory, SummaryMemory, EntityMemory
Sequential chains and branching with RunnableBranch
LangSmith: tracing, evaluation, dataset management

Tools Used

LangChainLangSmithPyPDFBeautifulSoup

Hands-On Lab

Build a multi-turn document Q&A application with conversation memory and LangSmith tracing for debugging

✓ Outcome: A conversational document assistant that remembers context across 10+ turns with full trace visibility

Phase 2 Capstone — AI Customer Support System

Multi-turn conversational bot with memory
Custom system prompt with role and constraint engineering
Structured JSON output parsing with validation
FastAPI REST endpoint with Swagger docs
LangSmith tracing dashboard for all conversations

Phase 3

Generative AI & RAG Systems

6 Weeks

Build production-grade Retrieval Augmented Generation (RAG) systems. Master vector databases, embedding models, advanced retrieval strategies and evaluation frameworks used by top AI engineering teams.

Week 9Vector Databases & Embeddings

8 topics

Topics Covered

What are embeddings — semantic meaning in vector space
OpenAI text-embedding-3-small/large, Cohere embed-v3, BGE, E5
Cosine similarity, dot product and L2 distance for nearest-neighbour search
Pinecone: namespaces, metadata filtering, sparse-dense hybrid search
ChromaDB: local vector store, persistence, collection management
Qdrant: payload filtering, on-disk storage, batch upsert
Weaviate: GraphQL interface, hybrid BM25+dense search
FAISS: flat, IVFFlat, HNSW index types — performance tradeoffs

Tools Used

PineconeChromaDBQdrantFAISSOpenAI Embeddings

Hands-On Lab

Build a semantic search engine over 10,000+ Stack Overflow answers — compare retrieval quality across 4 vector databases

✓ Outcome: Semantic search with <200ms p99 latency, MRR@10 metric tracking, and comparison dashboard

Week 10–11RAG Pipeline Architecture

9 topics

Topics Covered

Naive RAG vs Advanced RAG vs Modular RAG — evolution and tradeoffs
Document indexing pipeline: load → split → embed → store
Chunking strategies: fixed-size, recursive, semantic, parent-child
Hybrid search: BM25 + dense embeddings weighted combination
Re-ranking: Cohere Rerank, cross-encoder models, ColBERT
Context window management: lost-in-the-middle problem solutions
Query transformation: HyDE, step-back prompting, multi-query
Citation and source attribution in generated answers
Hallucination detection and grounding techniques

Tools Used

LangChainLlamaIndexCohere RerankBM25Okapi

Hands-On Lab

Build a production RAG system over a 500-page technical manual — implement hybrid search, re-ranking and citation extraction

✓ Outcome: RAG system with source-cited answers, faithfulness >0.85 and context relevance >0.80 measured by RAGAS

Week 12LlamaIndex & Advanced RAG

8 topics

Topics Covered

LlamaIndex vs LangChain — architecture comparison and when to use each
VectorStoreIndex, SummaryIndex, KnowledgeGraphIndex
Query engines: VectorStoreQueryEngine, RouterQueryEngine
Sub-question decomposition for complex multi-hop questions
Recursive retrieval and parent-document retrieval
RAGAS evaluation: faithfulness, context recall, answer correctness
Streaming RAG responses with async generators
Multi-modal RAG: images + text in a single pipeline

Tools Used

LlamaIndexRAGASOpenAI GPT-5.5Weaviate

Hands-On Lab

Build an advanced multi-document research assistant with RAGAS evaluation and a Streamlit dashboard showing live metrics

✓ Outcome: Multi-document RAG with automatic quality regression alerts when faithfulness drops below threshold

Week 13Multimodal AI & Image Generation

8 topics

Topics Covered

GPT-5.5 vision: image analysis, OCR, chart reading
Claude Opus 4.8 vision capabilities and use cases
DALL-E 3 and Stable Diffusion XL for image generation
Whisper API for speech-to-text (multilingual, timestamps)
TTS-1 and TTS-1-HD for natural text-to-speech
Building multimodal pipelines: audio → text → image → text
Invoice and document extraction with vision models
Cost and quality tradeoffs in multimodal applications

Tools Used

GPT-5.5DALL-E 3WhisperOpenAI TTSStreamlit

Hands-On Lab

Build a multimodal AI assistant that extracts data from invoice images, validates figures and exports to CSV automatically

✓ Outcome: Invoice processor with >95% field extraction accuracy on real-world documents

Phase 3 Capstone — Enterprise Knowledge Base

Vector database with 10,000+ indexed documents
Hybrid search with BM25 + dense + re-ranking
Source-cited Q&A with RAGAS evaluation dashboard
Multi-modal support (PDF + images)
FastAPI backend + React frontend deployed on cloud

Phase 4

AI Agents & Production Deployment

6 Weeks

Build autonomous AI agents and deploy production-grade AI applications. Learn multi-agent orchestration, tool use, and how to monitor and maintain AI systems in production — the skills that differentiate senior AI engineers.

Week 14–15AI Agents & Tool Use

8 topics

Topics Covered

What are AI agents — the ReAct (Reason + Act) framework
Function calling: tool definitions, parameter schemas, required/optional
Parallel function calling and multi-step tool use
Building custom tools: web search, calculator, database, file system
LangChain AgentExecutor and the new LangGraph framework
Agent memory: short-term (conversation), long-term (vector store)
Planning: task decomposition, sub-task generation
Agent evaluation: task success rate, tool efficiency, cost per task

Tools Used

LangChain AgentsLangGraphOpenAI Function CallingTavily Search

Hands-On Lab

Build an AI research agent that browses 10 websites, extracts information, deduplicates and writes a structured 2-page report

✓ Outcome: Autonomous research agent completing tasks 10× faster than manual browsing with citation accuracy >90%

Week 16Multi-Agent Systems

8 topics

Topics Covered

Multi-agent orchestration: why single agents fail at complex tasks
CrewAI: agents, tasks, tools, crews and processes (sequential/hierarchical)
AutoGen: conversable agents, GroupChat, human-in-the-loop
Agent roles: planner, executor, critic, memory manager
Communication patterns: broadcast, round-robin, directed
Guardrails: input validation, output filtering, safety checks
Debugging multi-agent pipelines with LangSmith traces
Evaluating multi-agent quality and cost efficiency

Tools Used

CrewAIAutoGenLangSmithLangGraph

Hands-On Lab

Build a 3-agent content system (researcher + writer + editor) that produces a 1,000-word SEO article from a single keyword

✓ Outcome: Multi-agent content pipeline producing publish-ready articles in under 3 minutes

Week 17–18Production AI Deployment

8 topics

Topics Covered

FastAPI for AI APIs: async endpoints, dependency injection, middleware
Pydantic v2: strict validation, custom validators, serialisation
Docker containerisation for AI apps: GPU support, model caching
Environment management: secrets, API keys, .env files
Rate limiting and cost control: per-user token quotas
Background task processing with Celery and Redis
Deploying to Azure App Service, AWS Lambda, and Google Cloud Run
GitHub Actions CI/CD for AI application deployment

Tools Used

FastAPIDockerCeleryRedisGitHub ActionsAzure App Service

Hands-On Lab

Containerise and deploy a RAG application to Azure — with CI/CD pipeline, health checks and auto-scaling

✓ Outcome: Live AI application URL with CI/CD: every git push auto-deploys to production in under 4 minutes

Week 19–20AI Monitoring, Evaluation & LangSmith

8 topics

Topics Covered

LLM observability: why traditional monitoring is not enough
LangSmith: project setup, trace collection, run filtering
LangSmith Evaluation: dataset creation, evaluators (correctness, toxicity)
A/B testing prompts: statistical significance, sample size calculation
Cost tracking: per-model, per-user, per-feature dashboards
Latency monitoring: p50/p95/p99, timeout budgets
Guardrails AI: input/output validation, sensitive data masking
AI application logging best practices for compliance and debugging

Tools Used

LangSmithGuardrails AIOpenTelemetryGrafanaPrometheus

Hands-On Lab

Set up full LangSmith observability + Grafana dashboards for a production RAG app — detect and fix a performance regression

✓ Outcome: Operational AI monitoring: automated alerts when quality drops, with runbook to investigate and fix

Final Capstone — Production AI SaaS Application

Multi-agent AI workflow (3+ agents: planner + executor + reviewer)
Production RAG + agent combined system
Dockerised FastAPI backend with authentication
CI/CD deployed on Azure or AWS
LangSmith monitoring with cost and quality dashboards
GitHub portfolio-ready repository with README, demo video

Hands-On

Real-World Projects

Portfolio-ready projects that employers recognise.

AI Customer Support Bot

Multi-turn conversational bot using GPT-5.5 with memory, custom system prompt and LangSmith tracing

PDF Intelligence Platform

RAG-powered Q&A over PDF documents — hybrid search, re-ranking, source citations and RAGAS evaluation

AI Research Agent

Autonomous ReAct agent that browses the web, deduplicates findings and writes structured research reports

Semantic Search Engine

Vector-based search over 10,000+ documents — OpenAI embeddings + Pinecone with BM25 hybrid retrieval

AI Resume Analyser

GPT-5.5 Vision parses resumes, scores candidates against job descriptions and generates interview questions

Multi-Agent Content System

CrewAI 3-agent pipeline: researcher → writer → editor producing SEO-optimised content in 3 minutes

Voice AI Assistant

Whisper speech-to-text → GPT-5.5 reasoning → TTS-1 speech synthesis — fully conversational voice bot

Enterprise Knowledge Base

Company document RAG with Entra ID access control, department routing, and RAGAS quality monitoring

AI Invoice Processor

GPT-5.5 Vision extracts fields from scanned invoices with >95% accuracy and auto-exports to Google Sheets

LLM Cost & Quality Dashboard

Real-time LangSmith + Grafana dashboard: cost per session, latency, faithfulness scores, A/B test results

AI Code Review Bot

GitHub Actions workflow: AI reviews every PR — suggests improvements, detects bugs, posts inline comments

Deployed AI SaaS Application

Full-stack FastAPI + React app containerised with Docker, deployed to Azure, with CI/CD and monitoring

Tech Stack

Tools & Technologies

Python 3.11OpenAI GPT-5.5Claude Opus 4.8Gemini 3 ProDeepSeek V4LangChainLlamaIndexLangSmithPineconeChromaDBQdrantFAISSFastAPIDockerHugging FaceCrewAIAutoGenLangGraphRAGASAzure OpenAIWhisperDALL-E 3Guardrails AIRedisGitHub Actions

You Prepare For

Certifications

🤖

OpenAI Developer

OpenAI API Developer Certification (prep)

🧠

Google Cloud AI

Professional ML Engineer (prep)

☁️

AWS CLF-C02

AWS Cloud Practitioner (optional add-on)

Bonus — Included Free

Bonus Learning Tracks

SQL & Data Engineering Basics

PostgreSQL fundamentals
Joins, CTEs and window functions
SQLAlchemy ORM
Data modelling for AI apps
ETL pipeline concepts

AI Product & UX Design

Building AI product specs
Prompt UX patterns
Evaluating LLM quality
AI product roadmapping
Responsible AI principles

Interview Preparation

100+ AI engineering interview questions
System design for LLM apps
LLM evaluation scenarios
Mock technical interviews + feedback
Resume and GitHub portfolio review

After This Program

Career Outcomes

Entry-Level (0–1 yr) · ₹4–8 LPA

Junior AI Engineer
Prompt Engineer
AI Application Developer
ML Operations Analyst

Mid-Level (1–3 yr) · ₹10–18 LPA

AI Engineer
Generative AI Developer
LLM Application Engineer
AI Solutions Architect

Advanced (3+ yr) · ₹20–40 LPA

AI Platform Engineer
Head of AI Engineering
AI Product Lead
GenAI Consultant

Ideal For

Who Should Enroll

Students & Fresh Graduates

Enter the fastest-growing tech field with 12 real AI projects. No prior ML or AI experience needed.

Software Developers

Add LLM, RAG and AI agent skills on top of your existing programming background. Commands a 40–60% salary jump.

Business & Product Professionals

Understand how to build, evaluate and deploy AI products. Bridge the gap between business needs and AI engineering.

FAQ

Frequently Asked Questions

Do I need programming experience to join?

Basic Python knowledge helps but is not required. Week 1 covers Python from scratch. If you can write a for-loop in any language, you'll be fine.

Which AI models and APIs will I use?

Primarily OpenAI (GPT-5.5, embeddings, DALL-E 3, Whisper) and Azure OpenAI. We also cover Claude Opus 4.8, Gemini 3 Pro, DeepSeek V4 and open-source models (Llama 4, Mistral 3) for cost-effective alternatives.

How much will API costs be during the program?

Estimated ₹800–1,500 over 5 months using OpenAI credits. We teach cost-optimisation from week 1 so you always know what you're spending. OpenAI's $5 free credit covers the first 2 weeks.

What makes this different from a YouTube course or Udemy?

Live mentor sessions, real project reviews, doubt-clearing sessions, mock interviews, portfolio guidance and placement support. YouTube teaches you to watch; we teach you to build and get hired.

Can I take this alongside a full-time job?

Yes — weekend batches are available. Plan for 15–18 hours/week: 3h live sessions + 5h self-paced LMS + 7h labs. Many of our learners are working professionals.

Join the Next Batch

Ready to start your journey?

Live batches start every month. Only 20 seats per batch — enroll early to secure your spot.

Enroll Now Talk to Admissions

No-cost EMILive mentorshipLMS access for lifePlacement support

Master AI & Generative AI Program

Live Instructor-LedPython for AILangChain & LlamaIndexRAG & Vector DBsAI AgentsLMS AccessPlacement Support

20 Weeks

4 Phases

12+ Projects

Live Mentorship

Real-World Projects

Portfolio-ready projects that employers recognise.

AI Customer Support Bot

Multi-turn conversational bot using GPT-5.5 with memory, custom system prompt and LangSmith tracing

PDF Intelligence Platform

RAG-powered Q&A over PDF documents — hybrid search, re-ranking, source citations and RAGAS evaluation

AI Research Agent

Autonomous ReAct agent that browses the web, deduplicates findings and writes structured research reports

Semantic Search Engine

Vector-based search over 10,000+ documents — OpenAI embeddings + Pinecone with BM25 hybrid retrieval

AI Resume Analyser

GPT-5.5 Vision parses resumes, scores candidates against job descriptions and generates interview questions

Multi-Agent Content System

CrewAI 3-agent pipeline: researcher → writer → editor producing SEO-optimised content in 3 minutes

Voice AI Assistant

Whisper speech-to-text → GPT-5.5 reasoning → TTS-1 speech synthesis — fully conversational voice bot

Enterprise Knowledge Base

Company document RAG with Entra ID access control, department routing, and RAGAS quality monitoring

AI Invoice Processor

GPT-5.5 Vision extracts fields from scanned invoices with >95% accuracy and auto-exports to Google Sheets

LLM Cost & Quality Dashboard

Real-time LangSmith + Grafana dashboard: cost per session, latency, faithfulness scores, A/B test results

AI Code Review Bot

GitHub Actions workflow: AI reviews every PR — suggests improvements, detects bugs, posts inline comments

Deployed AI SaaS Application

Full-stack FastAPI + React app containerised with Docker, deployed to Azure, with CI/CD and monitoring