Become a Generative AI Engineer

Generative AI Engineering

Build production GenAI systems from RAG pipelines and autonomous agents to deployed FastAPI APIs. 12 real projects — each a functional system, not a notebook.

8 months

12 Projects + 2 Capstones

Live Course

PythonOpenAI APILangChainLangGraphRAGVector DBsCrewAIMCPHugging FaceFastAPIStreamlitDocker

Roles you'll be ready for

GenAI EngineerAI DeveloperLLM EngineerAI Product Engineer

Industry range: ₹6–12 LPA(fresher, India — market benchmark, not a guarantee)

Modules

Duration

8 months

Mode

Live Online

Projects

12 Projects + 2 Capstones

Support

Career Prep

Curriculum

10 modules. Each module includes a hands-on project. The final module contains your capstone assignments.

🚀 Opening Weeks

Opening Weeks — Python & API Foundations

23Topics

No prior Python assumed. These three weeks build the foundation the entire track runs on — Python from zero through your first working LLM API call.

Installing Python and VS Code; creating a virtual environment with venv

Variables — integer, float, string, boolean — and why data types matter when calling APIs

if, elif, else — conditional logic; comparison and logical operators

for loop, while loop, range(), break, continue

Defining functions — def, parameters, return, default argument values, *args, **kwargs

Lists — indexing, slicing, append(), remove(), len(), list comprehensions

Dictionaries — key-value access, nested dicts, iteration

Reading and writing JSON with json.loads() and json.dumps() — the format APIs speak

pip install, requirements.txt — installing libraries, managing dependencies

try/except — handling errors when API calls fail or files are missing

python-dotenv — loading secrets from a .env file without hardcoding them

APIs in plain language: a menu you order from, not a kitchen you enter

HTTP methods: GET and POST — the two you will use constantly

Response status codes: 200, 400, 401, 429, 500 — what each means and how to handle it

requests library — making your first HTTP call in 4 lines

Rate limiting and exponential backoff — the right way to retry a failed request

Your first OpenAI API call — hello world, reading the response

The messages format: system, user, assistant roles — why each exists

Temperature and max_tokens — your first two generation parameters

What a token is and why it affects both quality and cost

response_format JSON mode — asking the model to return structured data

Pydantic BaseModel — defining a schema and validating the response against it

What an embedding is: a list of numbers that captures meaning; cosine similarity

Module 1

Python & API Fundamentals

1 Sample Project16Topics

Python built for production — the difference between code that works once in a notebook and code that runs reliably at 3am without anyone watching.

Module 2

LLMs & Prompt Engineering

1 Sample Project19Topics

How language models generate text, how to control output reliably, and how to engineer prompts that hold up across thousands of queries — not just one.

What you will learn

Build, evaluate, and deploy production RAG pipelines with hybrid search and reranking

Create autonomous agents using LangChain, LangGraph, and MCP tool protocols

Orchestrate multi-agent workflows with CrewAI for complex, multi-step tasks

Run and fine-tune open-source models locally using Ollama, Hugging Face, and QLoRA

Implement guardrails, PII detection, and hallucination mitigation for production safety

Instrument GenAI applications with Langfuse tracing and cost monitoring

Build and deploy FastAPI streaming endpoints containerised with Docker

Evaluate RAG system quality using RAGAS metrics without manual annotation

Job outcomes

GenAI EngineerAI DeveloperLLM EngineerAI Product Engineer

Industry range:₹6–12 LPA

Frequently asked questions

Ready to start your Generative AI Engineering journey?

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