Learn AI engineering by building

Learn Python from the ground up — scoped ruthlessly to what AI engineering actually uses — plus the engineering substrate every later phase leans on: typing, async, streaming, a little SQL, and containers.

A working engineer's mental model of LLMs, and real fluency with the APIs — hosted frontier models and local open-weight models — including streaming, failure handling, and cost.

Treat prompts as engineered artifacts — versioned, tested, measurable — and make LLMs produce output your code can actually consume: validated JSON and tool calls.

Build retrieval-augmented generation properly — embeddings, chunking, hybrid search, reranking, citations — and know how to measure whether retrieval is actually working.

Build agents three ways — a raw loop, a framework (LangGraph), and an MCP server — and develop judgment about when agency is warranted at all.

Make quality measurable and regressions catchable: golden sets, LLM-as-judge, tracing, and evals wired into CI.

Ship and operate AI systems: gateway patterns, caching, queues, self-hosted model serving, deployment, security, and cost engineering.

Prove the whole stack by shipping one complete product: designed, built, evaluated, deployed, observable, documented — the artifact that anchors your portfolio and your next interview.