AI Engineering Fundamentals
Outcome: junior AI engineer–ready
You don’t need a math degree to start here. “Do I need to learn ML math first?” is the most common thing that stops people from starting — and for this path, the answer is no. That question belongs to Path 3 (ML Engineering on AWS), which is about training and owning models. This path is about applied AI engineering: calling LLMs well, wiring them into real systems, and shipping things that work in production. If you can write Python and call an API, you can do every project below.
The order matters. Each project below builds a specific, real skill — not a toy demo — and the 12 are sequenced so each one assumes the skills from the ones before it. By the end you’ll have built and deployed a full AI assistant, not just followed a tutorial.
Start with the smallest real thing: a CLI that loads a PDF or text file and actually answers questions about it, with streaming output and exact source citations for every answer. This is the project that proves you can wire an LLM into a real tool a person could actually use — everything after this assumes you’re comfortable with that loop.
The baseline for any AI Engineer / LLM Application Developer role — proves you can take an LLM call from idea to a working CLI tool, not just a notebook experiment.
What's next
If you want to apply what you just built inside a real engineering job right away, Path 2 takes the same project-based approach and points it at workflows you already do every day, like code review and PR summaries. If your goal is owning full ML systems instead, training, serving, and monitoring, Path 3 covers that ground directly. Neither path requires you finish here first. AI-Augmented Engineering →