Assumed starting point: two or more years of manual or functional testing, comfortable with test cases, bug reports, and Postman. Not assumed: any programming, any command line, any Git. Pace: about seven hours a week. A learner who already writes Python can skip Modules 2 and 3 and finish in about nineteen weeks.
What the learner leaves with
- A GitHub portfolio with six labs and one capstone, each with a README a hiring manager can skim.
- An eval suite for a chatbot that runs on every commit and fails the build when quality drops.
- A red-team report with reproducible prompt-injection findings.
- A test plan template for AI features they can reuse on day one of the new job.
- A resume rewritten from "manual tester" to "AI quality engineer", and fifty interview answers.
How the hours were sized
Lecture hours cover watching and following along. Lab hours are hands-on with a checklist and a definition of done. Project hours are open-ended work reviewed against a rubric. Beginners typically spend about a third more than the listed lab time on Modules 2 to 4, so the weekly pace leaves room for that.
Every module names an exit criterion, written the way a test plan writes it: a thing the learner can now do, verifiable by the portfolio output.