I hold Anthropic's Claude Certified Architect credential, which means I've spent real time designing and building with Claude and LLM tooling — not just reading documentation. When I screen AI engineer candidates for MindHunt clients, that hands-on background changes what I actually test for. Here's the framework.
Why Standard Technical Screens Fail for AI Roles
Most technical screens for AI roles are built by people who haven't built production LLM systems themselves — so they default to vocabulary checks. "Do you know what RAG stands for? Have you used LangChain? What's a vector database?" These questions filter for exposure, not competence. In 2026, almost every candidate can answer them correctly, because the vocabulary is now widely known. That's exactly why it's stopped being a useful filter.
What I Actually Test For
1. Where they've said no to an LLM
I ask candidates to describe a case where they specifically decided not to use an LLM for something, and what they used instead. People with real production experience have several examples immediately available — deterministic logic beating an LLM on cost, latency, or reliability grounds. People without it either can't answer or describe a case that isn't really about that tradeoff.
2. How they know a system is actually working
Building an LLM feature is the easy part. I probe for evaluation methodology — how they measure quality beyond "it looked right in a demo." Strong candidates talk about eval sets, regression testing when prompts change, and tracking failure modes in production. Weaker candidates describe manual spot-checking, or nothing at all.
3. A specific production failure, in detail
I ask for a time something broke in production — a hallucination that mattered, a cost spike, a latency problem, an agent that got stuck in a loop. The specificity of the answer is the signal. Real practitioners can describe exactly what broke, how they found out, and what they changed. Candidates without production experience give vague or hypothetical answers.
4. Their mental model of the current landscape
I ask what's changed in their approach to building with LLMs in the last six months. This field moves fast enough that someone whose mental model is frozen from 18 months ago — even if it was excellent then — is missing context that matters now (newer models, changed cost structures, evolved agent patterns).
Red Flags I've Learned to Watch For
- Every answer is "yes, LLMs can do that." Real practitioners have opinions about where LLMs are the wrong tool. Unconditional enthusiasm is a tell.
- Vocabulary without depth. Correctly using terms like "RAG," "agents," or "fine-tuning" but unable to go one level deeper when asked a follow-up.
- No opinion on evaluation. If someone can't describe how they'd know their system degraded, they likely haven't run one at scale.
- Demo-only experience. A polished weekend project is a fine starting point, but it's not the same achievement as a system serving real users under real constraints.
What a Strong Answer Actually Sounds Like
The best candidates I've screened get more specific, not more confident, the deeper I push. They volunteer the tradeoffs they made and why, they can describe what they'd do differently next time, and they talk about cost and latency as naturally as they talk about capability. That combination — technical depth plus product judgment — is what actually predicts whether someone will ship something reliable, not just something impressive in a demo.
How MindHunt Applies This
Every candidate who reaches a client shortlist through our AI engineer recruitment service goes through this exact screening framework — on top of MindHunt's standard vetting process. If you're trying to hire an AI/LLM engineer and want a technical partner who can actually tell the difference, that's what this is for.
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Frequently Asked Questions
What is a Claude Certified Architect?
An Anthropic credential recognizing hands-on expertise designing and building systems with Claude and LLM tooling — applied experience, not a general AI certificate.
Why does this matter for recruiting?
A screen run by someone with real hands-on LLM experience can ask follow-up questions a generalist recruiter can't, which is where the actual signal shows up.
Is this screening used on every MindHunt AI search?
Yes, in addition to MindHunt's standard candidate vetting.
Written by
Vadym Lobariev
MindHunt is an AI powered recruitment firm for founders, C-level and hiring managers who are tired of posting and praying. We execute a proven sourcing process for your hardest roles and show you the work every week — so you can make hires with confidence, not hope.
