Cohorts forming for the 2026 intake — dates and fees confirmed before you commit.

Register interest →
Skip to content

Python for AI Engineering

Six weeks of Python written the way AI systems need it: typed, tested, async where it matters, and structured so an LLM can call it. For people who have programmed before and want the foundation the rest of the curriculum assumes.

  • 6 weeks
  • Live online
  • Beginner
  • English
  • Dates announced soon
Join the waitlist

Who should take this program?

You'll get the most from this if

  • Any prior programming, in any language
  • Willingness to work in a terminal

This is not for

  • Anyone who has never written code — start with a general programming course
  • Experienced Python engineers — go straight to LLM Engineering

What you will build

  • A typed, tested Python service that calls an LLM API
  • A CLI tool with proper error handling and retries
  • A small library packaged and installed from your own repo

What you learn, week by week

Module 1 — Environments and tooling

  • Set up reproducible environments with uv
  • Read a traceback and fix the actual cause
  • Use Ruff and type hints from day one

Module 2 — Data modelling with Pydantic

  • Model inputs and outputs as types, not dicts
  • Validate at boundaries
  • Fail fast with useful errors

Module 3 — Calling APIs properly

  • Write a client with timeouts, retries and backoff
  • Handle rate limits without crashing
  • Log what actually helps debugging

Module 4 — Async and concurrency

  • Know when async helps and when it does not
  • Run concurrent calls safely
  • Avoid the three common deadlocks

Module 5 — Testing

  • Write tests that catch regressions, not tests that mirror the code
  • Mock external services
  • Use fixtures well

Module 6 — Packaging and shipping

  • Package a library and install it from Git
  • Containerise a service
  • Hand code to someone else without a call

How this program handles evaluation and governance

Responsible engineering starts before any model is involved. This course establishes the habits the later ones depend on: validating at boundaries, failing loudly rather than silently, logging enough to reconstruct what happened, and writing tests that actually catch regressions. Skip these and every governance practice later becomes theatre.

Tools and stack you will use

  • Python 3.12
  • Pydantic
  • pytest
  • httpx
  • uv
  • Ruff
  • Docker

Fees

Request pricing

Fees depend on cohort, format, and whether this runs for an individual or a team. We'll send the full breakdown.

Get fee details

Questions people ask

Do I need to know Python already?

No. You need to have programmed in something. If you know Java, JavaScript, C++ or similar, you will be fine.

Is this a data science course?

No. It is software engineering for people who will build AI systems. No statistics, no model training.

What if I already write Python daily?

Skip this and start at LLM Engineering & RAG. We will tell you honestly after a short conversation.

Is there a capstone?

A small one — a working service you deploy. The large capstone belongs to the agentic track.

Ready to build AI that holds up under scrutiny?

Join the waitlist
Join the waitlist