AI Engineer

Three tracks in sequence: ML & AI Engineering, then Agentic AI Engineering, then Forward Deployed AI Engineering. You learn to build models and LLM systems, then to give them tools and memory, then to scope and ship them inside a client organisation. The broadest of the engineering routes.

3 courses · Beginner

The path, in order

  1. ML & AI Engineering — From Python basics to production ML and AI systems
  2. Agentic AI Engineering — Design, build, evaluate, and deploy production-grade AI agents
  3. Forward Deployed AI Engineering — Embed inside enterprise clients, scope ambiguous problems, ship production AI on their infrastructure

Optional foundations, if you are starting from zero

The path assumes you can already write Python. If you cannot, take these first.

  1. Programming & CS Foundations — How to think like a programmer, from zero, before any AI or math content
  2. Engineering Practices & Tooling — The Missing Semester for AI Engineers: git, the command line, APIs and the craft real jobs assume you have
  3. Data Analysis & Visualization — pandas, NumPy, and the daily skill of turning real messy data into an answer

What you become

An AI engineer who can build a model, wrap it in an agent, evaluate it, deploy it, and then do all of that inside someone else’s codebase and constraints.

Roles this path prepares you for

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