LIVE AI Engineering Bootcamp: Build, Deploy & Scale AI Products

Session 2 – LLMs, Embeddings & Transformer Architecture

How does an LLM work? · Transformer Architecture · Embeddings & Semantic Similarity · Key Parameters in LLMs

Output: Embedding & Semantic Similarity in depth Understanding

Session 3 – Vector DBs & RAG

Introduction to VectorDB · Qdrant Hands-on · RAG in Pure Python · Qdrant Advanced Ops

Output: Your First RAG Pipeline

Session 4 – LangChain, Docling & Chunking Strategies

LangChain Intro · RAG in LangChain · Docling Document Parsing · Hierarchical Chunking

Output: LangChain RAG Pipeline with Smart Chunking

Session 5 – Advanced RAG Techniques & Hands-On

Vector & Vectorless RAG Architectures · Hybrid RAG · SQL RAG · Graph RAG

Output: Advanced RAG, Hybrid Search, Reranking

Session 6 – Agentic AI Foundations

What are AI Agents? · Tool Calling & ReAct Loop · Building your first Agent with LangChain · Routing with Semantic Router · Memory in Agents

Output: Your First Tool-Calling Agent

Session 7 – Agent Orchestration & LangGraph

Introduction to LangGraph · Workflows using LangGraph · Conditional branches & loops · Build a ReAct Loop using LangGraph

Output: ReAct Agent Rebuilt in LangGraph

Session 8 – Multi Agent Systems

Introduction to Multiagent Systems · Subagents and hand-offs · Multi-Agent Architectures

Output: GitHub Agent - Multiagent system for GitHub automation

Session 9 – Evals, Guardrails & Observability

Online vs Offline Evals · Evaluating RAG Pipelines · Evaluating Agents · Guardrails · Interpretation of Evals

Output: Adding Eval harness to the projects.

Session 10 – Multimodal AI

Architecture of a multimodal AI model · Extract data from PDFs/images · Multimodal RAG System

Output: Multimodal RAG Extracting Structured Data from Documents

Session 11 – Deployment on AWS

Introduction to AWS's Agent Stack · Deploying LangChain on AgentCore · AgentCore Services

Output: Your Agent Deployed on AWS AgentCore

Session 12 – MCP

What is Model Context Protocol · MCP Architecture · Building MCP Servers · Building MCP Clients

Output: Your First MCP Server & Client

Session 13 – Context Engineering

Importance of Context in Complex AI Systems · Context Engineering Fundamentals · Improving Context Propagation in your agents · Hands-on with deepagents

Output: Compound AI system (LangGraph multi-agent + MCP server + RAG) code & LangSmith

Session 14 – Fine Tuning & Local AI

Fine-tune vs RAG: when to opt for what? · LoRA & QLoRA training · Synthetic dataset pipelines · What are SLMs? · Ollama

Output: Fine-Tuned SLM Running Locally with Ollama

Session 15 – Capstone Project Discussion & Assignment Showcase

Scoping and discussing capstone project ideas · Showcase of assignments/side projects completed so far

Output: Your Capstone Project Plan

Session 16 – AI Product Thinking and Reinforcement Learning

Turning AI capabilities into usable, valuable products · Thinking like an AI product builder, not just a model user · Core intuition behind reinforcement learning · Reward design for better behaviour

Output: Product Thinking & Reinforcement Learning Notes

Session 17 – Advanced AI Engineering & Security

Cost optimization at scale · Semantic caching · Smart model routing · OWASP Top 10 for AI · Defending your system from adversarial attacks

Output: AI Security & Cost Optimization Playbook

Session 18 – Stakeholder Management and System Design for AI

Managing expectations of stakeholders · How an AI System is different · AI System Design Patterns · Production caveats of an AI System

Output: AI System Design Patterns Reference

Session 19 – Voice AI

Real-time voice stack · Low-latency voice systems · Interruptions & silence handling · Multi-turn context recovery

Output: Voice agent (<800ms end-to-end, tested on 10 queries)

Session 20 – Inference Engineering

What is Inference Engineering · Runtime Optimizations: batching, caching, quantization, speculation · Infrastructure Optimizations: routing, load balancing, autoscaling · Serving an LLM Endpoint with vLLM

Output: Local SLM (Qwen 2.5-3B) as a production REST API with vLLM, plus a cost/latency router benchmark

Session 21 – Personal Branding & Mock Interview

LinkedIn & Twitter positioning · GitHub portfolio storytelling · Live Mock Interview with Dhaval Patel & Siddhant Pandey

Output: Personal Brand & Interview Readiness Checklist

Session 22 – Final Project Showcase

Presenting your capstone project to peers, mentors & industry guests

Output: Capstone Demo Day