I've hired 30+ AI engineers over my career.

Here's what I actually look for — and it's probably not what you think.

What gets you an interview: a GitHub profile with real projects (not tutorial clones). One or two deployed applications I can actually use. Clear technical writing (blog posts, README files). Open source contributions.

What gets you the job: you can explain WHY you made design decisions, not just WHAT you built. You acknowledge what you don't know instead of bluffing. You ask good questions about the problem before jumping to solutions. You write clean, tested code — not just correct code.

What doesn't matter as much as you think: number of certifications (I stop reading after 2-3). University prestige (skills > pedigree). Years of experience if not matched by depth. Kaggle rankings (nice to have, not decisive).

The interview mistake I see most often: candidates who can explain concepts but can't build things. I'll take someone who built a messy but working RAG system over someone who can flawlessly explain the theory but never deployed one.

What I wish more candidates did: asked about the actual problems they'd work on. Showed genuine curiosity about our technical challenges. Demonstrated they'd researched our product before the interview.

The AI job market favors builders. If you can build, deploy, and explain — you'll never lack for opportunities.

Show your work. Everything else follows.