From Software Engineer to AI Engineer in 75 Days.
Live cohort-based bootcamp that takes you from writing code to shipping production-grade AI systems — RAG, agents, multi-agent orchestration, fine-tuning, and deployment. Taught by practitioners. Built with rigor.
Three kinds of engineers get the most out of this.
Pick the one closest to you. We'll show you how the bootcamp translates into your reality.
Every tool real AI engineers use in 2026.
Not a "we'll mention these" list. You'll build, break, and ship with each of these before Day 75.
Five arcs. Twenty-two sessions. One shipped portfolio.
Each arc ends with a real project you'll submit, review, and keep. Click an arc to see what's inside.
- Welcome & Team Introductions
- Cohort roadmap & Learning flow
- Development Setup: GitHub, Colab, AntiGravity
- AI Engineering 2026 Market Trends
- AI landscape
- Python fundamentals & OOP
- REST APIs, FastAPI & LLM Integration
- Layers of AI Engineering stack
- Large Language Models: attention, tokens & context windows
- Embeddings & Semantic similarity
- Vector databases with Qdrant
- Model economics & Selection
- RAG pipeline & Document Ingestion
- LangChain Basics & LCEL
- Hybrid Search: Improving Retrieval
- Evaluation Basics: RAGAS, faithfulness, answer relevance, context precision
- What is ReAct?
- Tool calling & reasoning loop
- Memory systems for agents
- Reliability caveats in production
- Workflow state machines
- Conditional branches & loops
- Build your first Agent in Langchain
- Routing, guardrails & compliance (HIPAA, GDPR)
- Trajectory evaluations
- Traces, runs & structured feedback
- Eval datasets & monitoring
- Debug deployed AI systems
- Deploy apps to AWS
- Offline / online eval systems
- Human-in-the-loop review
- Detecting performance drift
- Beyond RAG agent evals
- MCP : Anthropic's Model Context Protocol
- How to build your own MCP servers?
- Connect external MCPs with your agents
- Importance of Context in complex AI Systems
- Context Engineering Fundamentals
- Improving Context Propagation in your agents
- Introduction to deepagents
- Multi-agent architectures
- Subgraphs & parallel execution
- Circuit breakers & fallbacks
- Workflow vs Agentic Architecture Design
- Understanding Multimodal AI
- Vision Transformers Architecture
- Building a mutimodal pipeline with Gemini
- Multimodal RAG: index and retrieve images alongside text
- Position yourself as an AI Engineer.
- GitHub portfolio storytelling
- Levereging LinkedIn & Twitter to build your brand.
- Text-2-SQL architecture
- Self-correcting SQL agents
- What are SLMs?
- Casestudies of SLMs in Production
- Popular SLMs & their forte
- Ollama & vLLM: Running Models Locally
- Build local AI API endpoint
- Protect focused time
- Reduce context switching
- Production AI failures & lessons
- Architecture tradeoff analysis
- Basics of real-time voice systems
- Low-latency voice systems
- Handling interruptions and latency
- Multi-turn context recovery
- Building a voice agent
- Cost optimization at scale
- Semantic caching
- Smart model routing
- Advanced error handling, retries & rate-limiting
- Fine-tune vs RAG: When to opt for what?
- LoRA & QLoRA training
- Synthetic dataset pipelines
- Data filtering & deduplication
- Turning AI capabilities into usable, valuable products
- Thinking like an AI product builder, not just a model user
- Core intuition behind reinforcement learning in real-world AI
- Reward design: designing rewards for better behavior
- OWASP Top 10 for AI
- Prompt injection attacks
- Hallucination drift risks
- Context poisoning defense
- Sandboxed tool execution
- Defending your system from adversarial attacks
- Build complete AI product
- End-to-end system showcase
- Production-ready architecture
- Final polish & review
- Present capstone to panel
- AI system design interviews
- Portfolio review feedback
- Live demo & critique
Eight shipped pieces. Each one goes on your GitHub.
Not toy assignments. Real systems with real evals, real tracing, real submissions.
Build a production RAG Q&A system
Pick any document set you care about. Build the full RAG pipeline: ingestion, chunking, embeddings, retrieval, and generation. Experiment with chunking strategies to improve ingestion & retrieval.
→ Architecture diagram
→ Set-up guidelines & README.md
Multi-Tool Agent with Trajectory Eval
Build an agent that uses at least 3 tools via function calling. Build a ReAct agent using LangChain. Then measure whether it takes the right path, not just whether it reaches the right answer.
→ Trajectory eval results
→ Set-up guidelines & README.md
Add a real eval harness to your system
Take your Project 1 RAG or Project 2 agent and give it the evaluation infrastructure that production systems actually need. Offline eval set, LLM-as-judge, guardrails, and automated red-teaming.
→ LLM-as-judge evaluator code
→ At least 1 working guardrail
→ Red-team findings report
A Multiagent System with MCP, RAG & Memory
Put it all together. Multi-agent LangGraph with at least two roles. One MCP server you built. RAG grounding. Persistent memory via checkpointer. Full observability via LangSmith.
→ Full code repo
→ LangSmith dashboard screenshot
→ 2+ agents with defined roles, 1 MCP tool, persistent memory
Ship a local SLM with a smart router
Deploy Qwen 2.5-3B as a production REST API using vLLM. Build a router that classifies each query — simple ones go to your local SLM, complex ones to Claude Sonnet. Measure and prove the cost savings.
→ Latency comparison (SLM vs Claude)
→ Cost analysis at 10K requests/day
→ Benchmark results
Voice agent or multimodal agent — your choice
Voice track: Build an agent that responds end-to-end in under 800ms across 10 real queries. Handle interruptions and silences.
Multimodal track: Build an agent that extracts structured data from 3 different document types with high accuracy.
→ Latency or accuracy metrics
→ Code repo
→ Edge case handling writeup
Fine-tune your own SLM from scratch
Generate a synthetic dataset for a narrow task. Curate it — dedupe, quality filter, diversity sample. Fine-tune Qwen 2.5-1.5B with LoRA using Unsloth and HuggingFace TRL. Show the before/after eval scores.
→ Training config + run logs
→ Before/after eval scores
→ 1-page findings report
Capstone — Production-ready AI system
A complete system that demonstrates the full cohort arc. Must include: RAG or multi-agent orchestration, an eval harness, SLM + frontier model routing, a security audit, and a real deployment. Judged by external AI engineers on production readiness.
→ Architecture walkthrough
→ Eval results
→ Cost model & unit economics
→ Security audit findings
The honest comparison.
Other paths can work. Here's exactly what you get from each.
Three practitioners. One split: Build. Orchestrate. Distribute.
Each teaches what they actually do day-to-day.
Dhaval Patel
Ex-NVIDIA. 17+ years in AI and data, 1.5M+ subscribers on YouTube teaching AI & ML. Core architect of the AI Engineering curriculum.
Hemanand Vadivel
Ex-Edgewell. Builds the bridge from engineering skill to hireable presence. Leads the career & craft side of the cohort.
Siddhant Pandey
AI Research Engineer. Runs the live labs and production code walkthroughs across every session. Your real-time debugging partner.
One price. Everything included
One cohort. One bootcamp. One investment.
Everything you'd reasonably want to know.
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The best time was yesterday.
The second best is now.
500 seats · 75 Days to Become an AI Engineer · 8+ Projects
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