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5★ · 122 Google reviews

AI Engineering Bootcamp

Build production-ready AI applications with LLMs, RAG, MCP and AI agents — and deploy them on Databricks and AWS

About This Course

Companies no longer only want people who can analyse data — they want people who can put AI to work on it. This bootcamp teaches AI Engineering the practical way: you build real applications from scratch using large language models (LLMs), retrieval-augmented generation (RAG), the Model Context Protocol (MCP), LangChain, LangGraph and the OpenAI Agents SDK, then deploy them on Databricks and AWS. It is designed specifically for data professionals, so every project connects AI to the kind of data platforms you already work with.

Course Highlights

Build production-ready AI applications from scratch, not notebook demos
RAG systems with context engineering, vector databases and proper evaluation
AI agents built from the ground up with the OpenAI Responses API and Agents SDK
Stateful, multi-step AI workflows with LangChain and LangGraph
MCP servers that connect AI assistants to your own data and tools
Deployment on AWS and Databricks, and a portfolio you can show at interview
Live weekend classes with doubt sessions and an assignment after every session
Certificate of completion

Projects You Will Build

AI SQL Assistant — ask questions in plain English and get validated SQL and answers from a real database
Enterprise RAG System — a document question-answering system with citations and measured accuracy
MCP Server — expose your own data and tools so AI assistants can use them safely
Multi-Agent Application — several specialised agents that plan, hand off and work together
AI Agents on Databricks & AWS — take an agent from your laptop to a live cloud deployment

Syllabus & Modules

How large language models work, tokens, context windows and embeddings, choosing a model, cost and latency trade-offs, and prompt and context engineering.
The OpenAI Responses API, structured outputs, tool and function calling, streaming responses, and handling errors, retries and rate limits.
Natural-language-to-SQL over a real database, grounding the model in your schema, validating generated queries, and guarding against wrong or unsafe SQL.
Chunking strategies, embedding models, vector stores, similarity search, metadata filtering and hybrid search.
Building an enterprise RAG pipeline, re-ranking, citations, context engineering, and evaluating RAG quality for relevance and faithfulness.
Chains, retrievers and memory in LangChain, then stateful graph-based workflows, branching logic and human-in-the-loop steps with LangGraph.
The agent loop, tools and planning, the OpenAI Agents SDK, guardrails, handoffs between agents, and tracing what an agent actually did.
How MCP works, building an MCP server that exposes your data and tools, connecting it to AI clients, and keeping access secure.
Orchestrating several specialised agents, and using Databricks AI capabilities to build agents that work on enterprise lakehouse data.
Packaging AI applications behind APIs, deploying on AWS, monitoring and cost control, then a capstone project and portfolio review.

Why Learn AI Engineering?

Generative AI has moved from experiments to everyday business tools. The shortage now is not models — it is people who can connect them reliably to company data, test them properly and run them in production. Data professionals are unusually well placed for this work because they already understand databases, data quality and business context. This bootcamp adds the engineering layer on top, preparing you for roles such as AI Engineer, Generative AI Developer, or an analytics or data engineering role with an AI focus. Placement assistance is included: help with your resume, portfolio and interviews.

Who Should Join?

Data analysts who want to add AI to their skill set
Data engineers and BI developers working with Databricks or the cloud
Data scientists who want to ship LLM applications, not just models
Python developers moving into Generative AI
Prerequisite: Python basics. New to Python? Start with Python & Mathematics.

Course Details

Starts 5 Sep 2026
Schedule Sat & Sun, 9:30 AM IST
Each Class 2–2.5 Hours
Duration 10–12 Weeks
Level Beginner to Advanced
Prerequisite Python Basics
Mode Online / Offline
Projects 5 Projects
Rating 5★ (122 reviews)
Certificate Yes
Placement 100% Assistance

What You Get:

Live weekend classes with doubt sessions
Recordings of every class
Assignment after each session
5 portfolio-ready AI projects
Certificate of completion
Resume & interview preparation
Placement assistance
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EMI options available

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Ready to Build With AI?

The weekend batch starts on 5 September 2026. Batches are small, so enrol now or ask us anything on WhatsApp.

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FAQs

Questions About the AI Engineering Bootcamp

You need Python basics: variables, loops, functions, lists and dictionaries. You do not need a machine learning background — the bootcamp starts from how LLMs work and builds up to advanced agent systems. If you are new to Python, take our Python & Mathematics course first.
The batch starts on Saturday, 5 September 2026. Classes run every Saturday and Sunday at 9:30 AM IST, each lasting 2 to 2.5 hours, and the bootcamp runs for 10 to 12 weeks.
Five portfolio projects: an AI SQL assistant, an enterprise RAG system, an MCP server, a multi-agent application, and AI agents deployed on Databricks and AWS. Each project is something you can demonstrate in an interview.
Yes. You receive a certificate of completion from Leaders Data Science Learning Center once you finish the coursework and projects. Placement assistance includes resume building, portfolio review and mock interviews. It is assistance, not a job guarantee.
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