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AI has made résumés and portfolios easier to inflate than ever. A candidate can polish a GitHub profile, pad a skills list, and walk into an interview sounding fluent in tools they've barely touched. Learning how to vet an AI engineer's portfolio is what protects you from that gap.
This guide breaks down the real signals to check, the red flags and green flags that separate genuine experience from AI-polished claims, and where portfolio review fits into your broader AI developer hiring process.
Portfolio vetting is harder right now because AI closed the gap between looking skilled and being skilled. A candidate can generate polished code and rehearsed answers to common technical questions in minutes, all pointing to abilities they haven't actually built. Hiring managers who once judged a portfolio by its output now have to test the judgment behind it too.
This isn't limited to junior candidates. AI tools can produce senior-level polish just as easily, so hiring an AI engineer at any level needs the same live-verification step this guide walks through.
The single most important check is whether a claimed performance metric survives one follow-up question. Ask how a latency, accuracy, or cost number was measured, and treat any answer that can't explain the measurement conditions as unverified.
Pick one piece of their project, a model choice, a caching layer, a training pipeline, and ask why they built it that way instead of the obvious alternative. AI-generated or copied work falls apart under that kind of live explanation. A candidate with genuine ownership traces their own decisions without needing notes.
Ask them to point to one pull request, ticket, or feature that was entirely theirs, not the team's. Answers like "we built" or "the team optimized," with no specific piece they can name, are the single most common inflation point in a technical portfolio.
A portfolio full of clean demo apps with no error handling, monitoring, or cost and latency tradeoffs reads like tutorial work. Ask what happens when the model gets bad input, or how they'd know if it silently degraded after launch. One messier project that shows what breaks in production says more about real experience than ten polished ones.
Pick the number they're proudest of and ask how it was measured, what the baseline was, and what tradeoff they accepted to get there. Real practitioners can defend a number under questioning; inflated ones can't.
For example, a candidate who claims a "40% latency reduction" should be able to name the before-and-after numbers and the load conditions they measured under, not just repeat the headline figure. That one follow-up question does more to separate real experience from a padded claim than the rest of the resume combined.
The clearest red flag is an instant, over-polished answer with no hesitation. The clearest green flag is a candidate who pauses, admits a gap, and works through the answer out loud.
This table checks the portfolio itself, a separate step from interview-stage screening. The same rigor matters even more when vetting an AI Security Engineer, where a missed red flag carries higher stakes, or when vetting a RAG Engineer, where production judgment is the hardest thing to fake.
KDCI runs this exact checklist at scale, through an internal skills assessment, before a candidate profile ever reaches a client.
Every engineer in our network goes through a live code walkthrough, a check on individual contribution, and a review of production judgment before we call them deployment-ready. You get the shortlist after that work is already done.
KDCI's hiring process runs 7 to 14 days from first contact to placement, at a flat monthly rate that lands about a third below a comparable local US hire. That's a fraction of the timeline most technical searches face: AI/ML engineering hires can take 58 to 90 days (and up to 10 to 16 weeks for senior levels) to fill through standard recruiting channels, due to intense demand for the role.
Working with KDCI starts with sharing your role requirements and must-have skills. From there, you receive a shortlist of pre-vetted, deployment-ready candidates, ready for your own final interview or portfolio check, and onboarding runs on a flat monthly rate with no separate placement fee. Because the vetting already happened, that final interview can focus on team fit instead of re-running the checklist from scratch.
Every check in this guide takes real time to run well, and KDCI has already run it before a candidate reaches you. That same live walkthrough, ownership check, and production review applies whether the role is a Computer Vision Engineer, a Data Annotation Specialist, or an AIOps Engineer.
Skip the vetting and meet engineers who already passed it.
Ask for a private walkthrough of a past project, even one from a previous employer under NDA. A candidate can usually screen-share and explain code they built without exposing proprietary details, and that live conversation carries the same weight as a public GitHub repo.
Certifications show a candidate studied a framework. A portfolio shows they used it under real constraints. Weight a certification as a signal worth checking, not a substitute for the live walkthrough and metric verification covered above.
A take-home tests whether a candidate can produce working code with time and resources on their side. It doesn't test whether they can explain their own decisions on the spot, which is why pairing both checks tells you more than either one alone.
Using AI tools well is often a green flag. The tool itself isn't the issue. What matters is whether the candidate can explain, defend, and adapt the code it produced, exactly what the live walkthrough in this guide already checks for.
