AI Engineer

Get your environment running and ship your first AI-powered app.

Learn how code actually runs — variables, loops, functions, and scope.

Master lists, dicts, sets, and comprehensions — and know when to use each.

Understand classes, inheritance, and dunder methods so LangChain source code stops being a wall.

Read and write files, call real APIs, and handle errors gracefully — the 'I can do real things now' milestone.

Organise code into importable packages with pip, virtual environments, and proper project layout.

Build the math intuition behind AI — functions and growth rates, vectors and dot products, matrices, and probability. Optional but recommended before the agent modules.

Containerize your work so it runs the same everywhere — a baseline skill for any backend or AI engineer.

Build your first agents using the LangChain framework — model calls, tools, and chains.

Upgrade to stateful, looping agents with LangGraph — the modern foundation for production AI systems.

Coordinate multiple specialised agents with the Agent SDK — supervisors, handoffs, and shared state.

Build long-horizon deep agents from first principles — planning, memory, and tool orchestration without a framework.

Learn the event-driven patterns that make AI systems reliable and scalable at production load.