Become an AI Engineer in 11 Weekends
A hands-on, live bootcamp covering the full modern AI stack — LLMs, RAG, agents, LangGraph, MCP, Azure, multimodal systems, and fine-tuning — plus the career skills that get you hired. Every session ends with something you can run, show, or ship.
11 weekends. 22 sessions. One transformation.
Every weekend is a complete unit: learn it Friday, deepen and ship it Saturday. Expand any weekend to see both sessions.
Aug 1 & 7Weekend 01Python Environment & LLM Fundamentals
S1: The Modern AI Landscape & Python for AI Work
Map the AI engineering role, set up a professional Python environment, and make your first calls to a frontier LLM.
S2: Inside LLMs: Embeddings & Vector Databases
How LLMs represent meaning — tokens, embeddings, and vector search — and how to put a vector database to work.
Aug 14–15Weekend 02RAG Architecture & Pipeline Engineering
S3: RAG from First Principles: Concept to Pipeline
Why retrieval-augmented generation works, chunking strategies, and assembling the pipeline step by step.
S4: LangChain in Practice: Production RAG Applications
Move from raw pipeline to a production-shaped application with LangChain's building blocks.
Aug 21–22Weekend 03Agentic Systems, Guardrails & Evaluation
S5: Foundations of Agentic AI
What makes an agent: tool use, reasoning loops, and planning patterns you can rely on.
S6: Guardrails, Routing & Evaluating AI Systems
Keep AI systems safe and measurable — input/output guardrails, routing, and automated evaluation.
Aug 28–29Weekend 04LLM Observability & Cloud Deployment
S7: Seeing Inside Your AI: Observability with LangSmith
Trace every call, measure cost and latency, and debug failures with LangSmith.
S8: Enterprise AI with Azure AI Foundry — Part 1
Deploy your work to enterprise-grade cloud infrastructure on Azure AI Foundry.
Sep 4–5Weekend 05Multi-Agent Orchestration with LangGraph
S9: Multi-Agent Systems with LangGraph
Design graphs of cooperating agents — supervisors, specialists, and shared state.
S10: Project Showcase 1 + Azure AI Foundry — Part 2
Present your first portfolio project publicly, then go deeper on Azure deployment patterns.
Sep 11–12Weekend 06MCP & Context Engineering
S11: The Model Context Protocol & Context Engineering
Connect agents to tools and data the standard way with MCP, and engineer context deliberately.
S12: Effective Communication & Stakeholder Management
Translate technical work into stakeholder language — scoping, updates, and demos that land.
Sep 18–19Weekend 07AI Product Architecture & Voice Pipelines
S13: AI Product Thinking + Context Engineering, Deepened
Think like an AI product engineer: user problems, product specs, and the context strategy behind them.
S14: Advanced Context Patterns & Intro to Voice Agents
Advanced context techniques, then the anatomy of a voice pipeline: speech in, reasoning, speech out.
Sep 25–26Weekend 08Multimodal Systems & Text-to-SQL
S15: Multimodal AI Engineering
Build systems that work across text, images, and audio with multimodal models.
S16: Personal Branding + Building a Text-to-SQL Agent
Grow your public profile as an engineer, then build a guarded natural-language-to-SQL agent.
Oct 2–3Weekend 09Production Hardening & Capstone Sprint
S17: Advanced AI Engineering Patterns
Caching, fallbacks, rate limits, structured outputs — the patterns that survive production.
S18: Capstone Sprint + Life Basics in the Age of AI
Scope and start your capstone, plus a frank session on working and living well alongside AI.
Oct 9–10Weekend 10Fine-Tuning, Local Inference & DSPy
S19: Fine-Tuning & Running Local Models
When and how to fine-tune, and how to serve open models on your own hardware.
S20: Deep Work, Time Management & Prompt Optimization with DSPy
Optimize prompts programmatically with DSPy, and build the deep-work system to sustain your growth.
Oct 16–17Weekend 11Technical Interviews & Capstone Showcase
S21: Mock Interviews & Career Guidance
Practice real AI engineering interview questions and get personalized career feedback.
S22: Final Projects Showdown
Demo day: present your flagship project to the cohort and guests.
What you walk away with
Deployed, portfolio-ready projects
RAG applications, autonomous agents, a text-to-SQL system, and a self-chosen capstone — live, linkable, and demo-ready.
A production skill set
LLMs, RAG, LangChain, agents, LangGraph, MCP, observability, Azure AI Foundry, multimodal & voice systems, fine-tuning, DSPy.
Career assets
Stakeholder communication, personal branding, deep-work systems, mock-interview experience, and a public portfolio.
Community
A cohort channel with daily support, plus access to session recordings and materials.
Learn from practitioners
The weekly rhythm
Core concept + live build
The weekend's core idea taught hands-on — you code along and leave with something running.
Deepen, extend, or level up
Labs, advanced patterns, or professional skills that compound what you built on Friday.
Ship & get support
A 60–90 min shipping assignment, community Q&A, and office hours. All sessions recorded.
Who this is for
Developers, data professionals, and career-switchers who can write basic code and want to become AI Engineers.
- You need
- ✓ Basic Python (variables, functions, loops)
- ✓ A laptop
- ✓ ~4 hrs/week outside sessions
- You don't need
- ✕ ML or math background
- ✕ Prior AI experience
Questions, answered
Are sessions recorded if I miss one?
Do I need prior AI or ML experience?
What language are sessions conducted in?
Will I get a certificate?
How is this different from recorded courses?
What tools or accounts will I need?
How do I access course content?
Code your AI future in 11 weekends
+ ~₹1,500–2,000 for required tool subscriptions (Codex, Claude, Hugging Face)