Development & Programming
In-Person
Online
beginner Level
AI Engineering: The Developer's Path
Starts 12 October 2026•6 months
Overview
About this programme
AI Engineering: The Developer's Path is a six-month program that takes learners from their first line of Python all the way to a deployed AI system. Unlike most AI courses, which assume comfort with software engineering, this path begins with a full month on Python fundamentals — making it genuinely accessible to novices while still rewarding for developers who want to solidify their foundation. From there, it builds through database design, API development with FastAPI, machine learning concepts, and then the technologies that define modern AI: large language models, prompt engineering, LangChain, retrieval-augmented generation, and autonomous agents with LangGraph.
Where most AI courses stop at "it works on my machine," this one continues into the disciplines that separate prototypes from products: AI-specific user experience patterns, evaluation frameworks and testing, debugging LLM applications, cost engineering, and full MLOps including containerization, cloud deployment, observability, and compliance. Learners finish with advanced topics — fine-tuning with LoRA and QLoRA, multi-modal AI, and AI safety — before consolidating everything into a portfolio-worthy capstone project.
This program is designed for curious beginners, career changers, and working software developers who want to become the AI engineer on their team. By the end, learners will have built, evaluated, and deployed a complete end-to-end AI application, and will be fluent in the architectural decisions — build vs. buy, which model for which task, when to use RAG vs. fine-tuning, what to measure — that real AI engineering work requires every day.
Curriculum
Programme structure
What you'll learn
- Write Python confidently from first principles — no prior programming experience assumed.
- Design relational databases and query them fluently with SQL.
- Build and ship backend APIs with FastAPI, using Git and GitHub the way teams actually do.
- Understand classical machine learning well enough to know when it, and not an LLM, is the right tool.
- Work with large language models directly, and engineer prompts that hold up outside a demo.
- Build retrieval-augmented generation systems that answer from your own data.
- Design autonomous agents with LangGraph, and the user-experience patterns that make AI features usable.
- Evaluate, test and debug AI systems — including what they cost to run, and how to bring that cost down.
- Deploy to production with MLOps practices, and ship a capstone project you can show an employer.
17 modules
- Master the fundamental tool that every professional developer uses daily. Learn to track changes, collaborate with others, and manage your code like a pro from the very beginning of your development journey.
- Understand version control concepts and why Git is essential for modern software development
- Use GitHub effectively for remote repositories, collaboration, and showcasing your work to potential employers
- Master Git basics including repositories, commits, branches, and merging for effective code management.
You'll build: a version-controlled project on GitHub, with branches, pull requests and a history an employer can read
Module 1: Version control with Git and Github
Master the fundamental tool that every professional developer uses daily. Learn to track changes, collaborate with others, and manage your code like a pro from the very beginning of your development journey.
- Understand version control concepts and why Git is essential for modern software development
- Use GitHub effectively for remote repositories, collaboration, and showcasing your work to potential employers
- Master Git basics including repositories, commits, branches, and merging for effective code management.
You'll build: a version-controlled project on GitHub, with branches, pull requests and a history an employer can read