Who this track is for
The AI Engineer track is built for developers who want to ship AI products. You’ll learn how to work with large language models, build retrieval-augmented generation systems, design multi-agent architectures, and deploy AI systems that actually work in production. Best fit if you:- Come from a software engineering background
- Want to work at AI-native startups or build your own AI product
- Are interested in the systems side of AI, not just research
Curriculum
Level 1 — LLM Foundations (free)
| Module | Key Topics |
|---|---|
| Working with LLMs | APIs, tokens, context windows, system prompts, few-shot prompting |
| Prompt Engineering | Chain-of-thought, structured output, evaluation, red-teaming |
Level 2 — Building with LLMs
| Module | Key Topics |
|---|---|
| RAG Fundamentals | Chunking, embeddings, vector search, pgvector, retrieval quality |
| LangChain & Pipelines | Chains, memory, document loaders, evaluation |
| Structured Output & Function Calling | JSON mode, tool use, schema validation |
Level 3 — Production AI Systems
| Module | Key Topics |
|---|---|
| AI Agents | ReAct loop, tool use, agent memory, multi-step reasoning |
| Multi-Agent Systems | Orchestration, specialised agents, handoff protocols |
| LLM Evaluation | Benchmarking, LLM-as-judge, regression testing |
Level 4 — Advanced AI Engineering
| Module | Key Topics |
|---|---|
| Fine-tuning & Alignment | LoRA, RLHF basics, dataset curation, evaluation |
| AI Infrastructure | Cost management, caching, streaming, observability |
| Production Patterns | Rate limiting, fallbacks, human-in-the-loop, guardrails |
| Multimodal Systems | Vision, audio, document understanding |
Level 5 — Capstone
Build a complete, production-ready AI product — from architecture design through deployment. Peer-reviewed by your cohort.What you’ll be able to build
- RAG systems — document Q&A, semantic search, knowledge bases with real retrieval metrics
- AI agents — autonomous systems that use tools, maintain memory, and complete multi-step tasks
- LLM pipelines — evaluation frameworks, structured output, production-grade APIs
- Complete AI products — end-to-end systems with proper error handling, observability, and cost controls