Specialist recruitment for LLM engineers, ML engineers, and AI-native product teams — screened by a founder who is a Claude Certified Architect, not a generalist recruiter guessing at buzzwords.
AI hiring is full of inflated titles and copy-pasted resumes. We know the difference between someone who can talk about RAG and someone who has shipped it in production.
Standard tech recruitment doesn't work well for AI roles. Here's why companies struggle — and why a specialist screen changes the outcome.
"AI Engineer," "ML Engineer," "Prompt Engineer" and "AI Product Manager" mean different things at different companies. Most recruiters can't tell them apart — we can, and we'll tell you which one your role actually needs.
LLM engineering — RAG, agents, evals, fine-tuning — has existed as a distinct skill since roughly 2023. There's no "10 years of LLM experience." Depth has to be assessed on production judgment, not tenure.
Everyone added "AI" to their resume in the last two years. Filtering real production experience from a weekend hackathon project takes someone who can ask real technical questions, not keyword-match.
The best AI engineers are heads-down shipping, not job hunting. And the field moves fast enough that "senior" from 18 months ago may already be behind on tooling that matters today.
We don't keyword-match resumes for "LLM" or "GPT." Every candidate goes through a structured technical screen before they reach your desk.
Founder Vadym Lobariev holds Anthropic's Claude Certified Architect credential — hands-on experience building with Claude and LLM tooling, not just reading about it. That means technical screens that test real AI/LLM fluency.
Same guarantee as every MindHunt placement — if a hire leaves within 90 days, we start a new search on your behalf.
We place AI/ML talent for European and US companies, sourcing across Ukraine, Poland, Germany, and beyond.
"AI Engineer" means different things to different companies. We scope the right role for what you actually need.
Builds and ships LLM-powered features: RAG pipelines, agents, tool use, evals, prompt and context engineering.
Trains, fine-tunes and deploys models. Strong in the underlying statistics, data pipelines and infrastructure that LLM tooling sits on top of.
Owns the pipelines, monitoring, cost control and scaling behind AI systems once they're in production.
Translates LLM capabilities into product decisions — understands both what the technology can do and where it breaks.
Six steps. Full transparency. Weekly updates.
We map what your product actually needs from an AI hire — often a narrower, more specific brief than "someone who knows AI."
We identify credible candidates across the roles that fit: LLM engineers, ML engineers, MLOps, AI product — not just whoever has "AI" in their headline.
The strongest AI engineers aren't applying to job boards. We reach them through direct, personal outreach.
Structured technical assessment run by someone who actually builds with this technology — see "Screened by a Claude Certified Architect" below.
You receive fully vetted profiles with our written assessment of each candidate's real (not claimed) AI/LLM depth.
We guide the offer process and stay involved through onboarding to make sure the hire lands well.
Tell us about the role and we'll map the market for you — no commitment required for an initial conversation.
90-day replacement guarantee · Confidential search · Screened by a Claude Certified Architect