Q: QA Engineer / SDET → AI Engineer

Your head start: evals. Every AI team eventually faces the question "how do we know the system is working?", and answering it takes exactly the mindset you've spent years building: designing test cases, thinking in edge cases, defining what "good" means, refusing to ship on vibes. In AI engineering that discipline is called evals, and it's one of the most in-demand and least-supplied skills in the field.

Your gap: production code. You've spent your career testing systems; now you'll be building them — APIs, app logic, deployment. That's learnable in months, and you already work in engineering environments and know how software ships. Aim at the part of AI engineering where quality thinking is the scarcest resource, and your background stops being something to explain away.