Hire AI developers, LLM engineers and ML engineers across Europe and Ukraine — every candidate technically 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 hire for US, European, Japanese and Australian companies, sourcing AI engineers in Ukraine (Kyiv, Lviv), Poland (Warsaw, Kraków), Germany, Portugal and the wider EU.
"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. What most companies mean when they say they want to hire AI developers.
Designs agentic systems that plan, call tools and act: MCP servers, tool schemas, orchestration, guardrails, and the evaluation that keeps agents reliable outside a demo.
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.
Hands-on technical leader who builds the shared AI services layer — APIs, orchestration, observability, cost control — and the team around it.
Runs the AI tooling your own engineers depend on: coding agents, MCP servers, plugins, access and cost limits — and gets teams from "tried it once" to daily use.
Specialists in a modality: image and video models, language pipelines, or speech-to-text systems such as Whisper running in production.
Statistical analysis, experimentation and exploratory modelling to answer business questions — distinct from the engineer who productionizes the result.
Translates LLM capabilities into product decisions — understands both what the technology can do and where it breaks.
Looking specifically for Claude Code or agentic-AI experience? See our dedicated Claude Code developer search.
Getting this wrong is the most expensive mistake in AI hiring: you interview ML researchers for a job that is 90% API integration, or the reverse.
| Role | What they build | Hire when | Typical background |
|---|---|---|---|
| LLM / AI Engineer | Product features on top of foundation models (RAG, agents, evals) | AI is a feature or the core of your product and a model API can do the job | Backend or full-stack engineer with 1–3 years of production LLM work |
| ML Engineer | Custom models trained or fine-tuned on your data | A foundation model API genuinely can't do what you need | Statistics, data pipelines, model training and evaluation |
| MLOps / AI Infrastructure | Deployment, monitoring, cost and scaling for AI systems | AI is already in production and reliability or cost is the problem | DevOps or platform engineering plus ML tooling |
| AI Platform Lead | A shared AI services layer used by several product teams | More than one team is shipping AI and each is reinventing the plumbing | Senior engineer with team-lead experience and shipped AI features |
| AI Enablement Engineer | Internal AI tooling: coding agents, MCP servers, shared prompts | You want your existing engineers to work faster with AI | Working developer who moved into developer tooling |
Numbers from two AI searches we ran in 2026. The market is large on paper and small in practice — this is why keyword matching fails.
Hands-on technical lead, remote within the EU, for a European B2B SaaS company
Nearly half of all rejections came down to the same thing: the title said AI, but the hard requirements — production AI features plus experience leading a team in a distributed system — were not there. Only 9% were rejected on salary.
Remote within the EU, reporting to a VP of Engineering
A role this new has no established talent pool and nobody applies for it. Every candidate came from direct outreach, and 71% of those who declined were simply not open to moving — which is why the first hundred conversations produce a handful of interviews, not dozens.
Approximate annual cost to the employer for a senior AI engineer with production LLM experience. Same seniority, very different budgets.
| Where you hire | Approx. annual cost | Basis |
|---|---|---|
| United States | $225,000–$300,000 | Employee, salary plus employer costs |
| United Kingdom | £100,000–£150,000 | Employee, incl. employer National Insurance |
| Germany | €105,000–€165,000 | Employee, incl. social contributions |
| Poland | €55,000–€80,000 | B2B contractor invoice |
| Ukraine | $66,000–$90,000 | Contractor (FOP) invoice |
Sources, methodology and junior-to-senior figures: AI Engineer Salary 2026: US, Europe & Ukraine Compared. Hiring in Ukraine specifically? See how we hire developers in Ukraine.
Six steps. Shortlist in 2–3 weeks. Full transparency and 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.
Sourcing and assessment usually take 2–3 weeks. You then 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.
The searches on this page were run in MindHunt AI — the sourcing and pipeline platform we built for our own agency work. It is available by subscription for in-house recruiters and talent teams who want the tooling without the agency.
Explore MindHunt AI →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