Builds the machinery behind the magic — retrieval, orchestration, monitoring — so your AI works in production, not just in the demo.
Wires models into your product properly: streaming, fallbacks, rate limits, cost controls.
Retrieval that actually retrieves — chunking, embeddings, vector search, reranking over your real data.
Multi-step agents with tools, memory and state — built on frameworks or bare metal, whichever survives production.
Quality dashboards, regression suites and alerting so behavior drift is caught before customers catch it.
Model routing, caching and prompt budgets that keep the unit economics sane at scale.
Keeps customer data where it belongs — tenancy, PII handling, audit trails.
The classic moment: the proof-of-concept impressed everyone, and now it has to survive real traffic, real data and a real bill.
Every AI engineer on our bench has passed a role-specific battery — designed for the AI era, where the portfolio matters more than the years.
Design retrieval over messy, real-world data — we probe the trade-offs, not the buzzwords.
Candidates walk us through production LLM code they wrote — and defend the decisions.
Incidents, drift, cost blowups: we check they've felt production, not just tutorials.
If they can't say how they'd measure quality, they don't reach the bench.
Every placement starts with agreed outcomes. This is the typical shape for an AI engineer — calibrated to your context in the intro call.
Audits the current pipeline, adds evals and observability — you finally see cost, latency and quality per feature.
Rebuilds the weakest link (retrieval, orchestration or fallbacks); reliability and unit economics improve measurably.
Production hardening done: monitoring, provider fallbacks, cost budgets — the AI feature stops being the fragile part.
Straight answers to what clients ask before the first placement — the rest is a 30-minute call.
All major ones — OpenAI, Anthropic, Google, plus open-weight models where data or cost demands it. Provider-agnostic architecture with fallbacks is part of the standard vetting.
Usually — most 'hallucination problems' are retrieval and eval problems in disguise. The fix starts with measuring where answers fail, then repairing the retrieval and grounding chain, not just tweaking prompts.
Classic CV screening barely works for roles this new — so we vet on artifacts and exercises instead: shipped work, live design or build tasks, and evaluation-writing. The battery is role-specific and documented, so you see exactly what was tested.
The first 60 days are the proof: if the match isn't right, the placement ends without long-term obligation — or we replace the profile. The risk of a wrong hire stays with us, not with you.
From our network across the Netherlands, Ukraine, Hungary, Romania, Poland and Bulgaria — CET ±1 and fluent English, so they join your standups live. Probegin contracts and payrolls them; you get one Dutch counterparty.
A 30-minute brief is all we need. Two to three vetted profiles follow — you interview, you choose.
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