The pool of engineers who build and deploy AI systems is considerably smaller than hiring managers with a generalist view of the market expect. After all, a quick glance at LinkedIn reveals thousands of profiles with “AI” credentials, only that the vast majority have cursory knowledge of AI development, infrastructure, and architecture.

Most candidates use AI instead of actively pushing its boundaries.

Resume inflation compounds the problem. The rapid popularisation of AI tools has produced a large cohort of professionals who rushed into online courses to complete a couple of demo projects. Often, that’s enough to add “machine learning engineers” to their profiles.

Deep-fake candidates are another emerging phenomenon. Impressive-looking resumes, scant LinkedIn profiles with a handful of connections, and, should they reach an interview, sketchy-looking video and even sketchier manner of speaking.

Distinguishing these groups from AI engineers with real hands-on experience in development and deployment requires specialised technical market knowledge, which companies that don’t have AI as their core domain may find hard to maintain internally.

An AI specialist's knowledge should be judged on specificity and depth of expertise. Many of the AI specialists of today were backend, platform, or systems engineers yesterday, so evaluating their expertise requires precise questioning and technical understanding.

A baseline framework with three core layers of questioning, each designed to close the escape hatches left open by the one before it, can paint a full picture. All the same, any framework should be used for structure and guidance toward clarity, not for narrowing the discussion. The most suitable candidates often feel comfortable discussing topics such as application architecture, production incidents, trade-offs, and business outcomes.

They willingly venture into specifics.

Here’s an example of an evaluation framework for an AI engineer.

The tell, across all three layers, is always specificity: dates, company names, incident details, tool names. Generalities signal studied preparation. Specifics signal lived experience.

The first layer tests what the candidate has actually shipped. Experimentation is an important part of learning, but it cannot be the only real-world experience.

Questions form a single interrogation thread, each one closing an escape hatch left open by the previous answer:

A successful candidate should answer all questions specifically and in detail, to demonstrate ownership and involvement. Failing any of the questions strongly indicates lack of experience in production at scale.

The assessment should evaluate how the AI specialist works today, not only what they’ve built in the past. Questions around agenting tooling in the daily workflow are quite revealing.
Is agentic coding their default mode, not a novelty they occasionally reach for? How do they incorporate it in their day-to-day workflow?

Candidates who have built custom tools, MCP servers, orchestration layers, evaluation systems, or execution environments show they understand AI agents as composable systems, not just chat interfaces.

The third line of questioning moves beyond “have you done it” to how the candidate understands and handles difficulties.

Such knowledge stems from shipping both pipelines and autonomous systems and learning the trade-offs firsthand, from grounded, real-world use cases.

Someone who only built basic RAG or prompt wrappers typically has no real answer to these questions, which is exactly the evaluation gap we've been navigating at ICLR.

Lucky Hunter’s AI specialist recruitment process is built around proactive sourcing. Not only do we research candidates before they change their LinkedIn status to “Open to work”, but we find them before they create a LI profile.

Our pool of candidates is built through live contact, industry awareness, monitoring of research and academia, and showing up where they do.

The International Conference on Learning Representations (ICLR) is one of the most competitive peer-reviewed venues in machine learning. Acceptance rates for full papers sit below 28%. In April 2026, Lucky Hunter attended the ICLR in Rio de Janeiro as the official recruiting partner of 42.com.

We had the chance to meet AI specialists at the intersection of research excellence and advanced applied knowledge.

Lucky Hunter entered the conference with about 1,000 candidates and emerged with over 2,000 AI specialists, heavily skewed toward mid and senior positions. The gap between “worked with AI tools” and “can build frontier AI systems” was really obvious at ICLR.

The strongest profiles were concentrated around research labs, infra teams, and academic ecosystems, which underlines once more the higher-quality connections conference networking produces.

Conference sourcing leads to a qualitatively different candidate profile:

Beyond ICLR, Lucky Hunter maintains an active network of vetted AI professionals across Europe, North America, and Asia, built over years of specialised technical recruiting.

Not all AI recruiting agencies operate at the same depth. Our partners at 42 appreciate our flexible, thorough screening of niche ML specialists, but approaching a new hiring agency always comes with uncertainty.

Here are the five key factors a CTO or Head of Engineering should consider when evaluating potential recruitment partners.

If the recruiter cannot explain the difference between RAG and fine-tuning without a prompt, they wouldn’t be able to tell a genuine specialist from a surface-level vibe-coder.

A recruiter’s network with 1,000 verified, actively maintained warm relationships with AI specialists is orders of magnitude more valuable than 10,000 LinkedIn connections. Ask specific questions: How many roles requiring production LLM deployment have you filled in the past 12 months?

Ask the recruiter to describe their technical screening process in detail. If the answer is vague or centres on "culture fit," the vetting is not specialised. A rigorous AI talent acquisition partner can articulate exactly what they test and why.

Recruiting AI specialists isn’t a volume exercise. The right candidates aren’t refreshing job boards. Instead, they present papers at the ICLR, contribute to open-source projects, and build systems that reduce cost and improve reliability.

Access to the AI ecosystem and deep understanding of this frontier market pave the road to success. Our vetting process includes resume screening and reference checks, but the cutting edge lies elsewhere.

We know the internal dynamics of the AI industry, know who to look for, and we speak the right language, which helps us align with employers and AI specialists.

Our intimate knowledge of the industry helps identify strong AI engineers beyond LinkedIn and traditional job boards.

We source candidates at research conferences and vet technical claims against real-world evidence. We maintain a network of verified practitioners, not a database of generic resumes.