AI-native engineering hire
Hire LLM engineers to own quality, latency and cost in production.
Find senior engineers who can take an LLM feature from model choice to a reliable release. Finalists have passed all agreed technical and soft-skill stages, with interest, compensation expectations and availability confirmed.
First qualifying finalist by business day 15, or stop the search for a full $2,000 refund. The search runs for 60 calendar days from the agreed launch date while the vacancy remains open.
Standard recruitment
Your search at a glance
Standard: 18% of first-year salary. $2,000 is paid at launch and credited toward that total; the balance is due when your hire starts. We agree the salary basis and search scope before payment.
60-day search, launch-payment refund and replacement terms
- Seniority
- Senior LLM engineer searches; the technical bar is agreed in your brief.
- Compensation guide
- Indicative planning band: €62–105K a year in senior B2B contractor compensation across CEE.
- Search markets
- CEE candidates, with location and working-hour requirements agreed for your team.
Compensation is indicative. We agree the salary band, locations and working-hour overlap before search; the placement fee is separate.
Focus your engineers’ interview time
We assess technical and soft skills before introducing candidates, so your engineers meet people who have passed the agreed requirements. Review the evidence, run your own technical and team interviews, and choose who to hire. We support coordination and the offer process.
Choosing the role
When an LLM engineer is the right hire
An LLM engineer owns the behaviour of a language-model feature in your product: model selection, prompts and context, output evaluation, application integration and operational monitoring. A useful brief starts with the user task and its acceptable failure modes. A drafting assistant, a document-extraction service and a support chatbot require different evidence even when they use the same model provider.
For a product-facing role, prioritise someone who can explain the full request path and work with your backend and product teams. For model adaptation, specify the training data, evaluation objective and deployment constraints. Fine-tuning experience is relevant when that work is in scope; using a managed model well can also be the right production decision.
Choose a RAG engineer when finding the right source material is the main bottleneck. Choose an ML platform engineer when deployment reliability or shared inference infrastructure needs a dedicated owner. An LLM product engineer can cover adjacent work, but the brief should identify which area needs depth from day one.
Bring to the intake: the feature you need to ship or improve, representative inputs, current quality concerns, latency and cost budgets, your deployment environment and who makes the release decision. A redacted architecture sketch is enough to start. We turn these constraints into a screening scorecard before approaching candidates.
Across Recruo searches
Our recruitment results so far
Company-wide observations reported by Recruo’s founder on 11 September 2026. These describe our overall experience, rather than a separate cohort for this role or region.
- Pass every client assessment stage
≈9/10
Presented candidates who successfully pass the client’s full assessment process, including technical interviews.
- Candidates the client would offer to
≈4/5
Client willingness to make an offer after assessment. Offers sent, accepted and hires are separate outcomes.
- Completed probation periods passed
100%
All placed candidates whose probation has concluded have passed so far. Ongoing probation periods are excluded.
How to read these results
Pass every client assessment stage: Candidates who pass all client assessment stages, relative to candidates presented. Around 9 in 10 is an approximate company-wide observation, not a published sample of exactly ten people. Offers, acceptance and starts are separate outcomes; sample size, reporting period and treatment of ongoing assessments are not yet published.
Candidates the client would offer to: Presented candidates the client considers suitable for an offer, divided by candidates presented. Around 4 in 5 describes willingness to offer; it does not mean four offers or hires for every five candidates.
Completed probation periods passed: Placed candidates who passed probation, divided by placements whose probation period has ended. Probation lengths vary by engagement; this is not a 90-day retention measure or a guarantee of future outcomes.
Sample sizes and the reporting period are not yet published. The candidate ratios are approximate; none of these results is a forecast for your vacancy.
Example scorecard
Evidence to look for in your shortlist
Illustrative assessment criteria, not a candidate profile or a record of a completed placement. The final scorecard is agreed for your role.
Example scorecard: senior LLM product engineer
Role scope: Use this scorecard to assess whether a candidate can own the LLM feature in your brief and where another specialist would be needed.
- Product ownership: explains the user task, acceptance criteria and their contribution from implementation through release.
- Model decisions: compares prompting, retrieval and model adaptation against the same task-specific baseline.
- Evaluation: shows how representative examples and human review inform a release decision.
- Operations: can diagnose latency, provider failures and cost changes, with a practical fallback or rollback plan.
- Team fit: communicates tradeoffs clearly and can work within the agreed collaboration hours and delivery process.
How we source
How we assess LLM engineering experience
We source engineers whose previous responsibilities match your intended scope, using targeted outreach, referrals and relevant technical communities. Product ownership and sound engineering decisions matter more than the title on a CV or familiarity with a particular orchestration library.
The technical screen asks candidates to explain a feature they worked on: what they personally owned, how quality was measured, which tradeoff they made and what happened when the system failed. Useful follow-ups include how they separate model errors from integration errors, compare a cheaper model fairly, and roll back a prompt or model change. AI supports the interview; a human recruiter reviews the evidence and unresolved questions before shortlist delivery.
Evidence can be a confidential project walkthrough, a redacted design document, a relevant work sample or public work the candidate chooses to share. We do not require open-source contributions or a minimum public user count. The shortlist gives your team the role fit, technical evidence and areas to explore in its own interview; references can be agreed with the candidate where relevant.
Role-specific screening
What we assess
The key hiring decision is the depth you need in application delivery, model adaptation and operations. We assess that balance against the actual work.
Output quality
Task-specific evaluation and failure analysis
Role-specific screening criteria agreed during intake
Production ownership
Release, monitoring and recovery decisions
Role-specific screening criteria agreed during intake
Engineering judgement
Quality, latency and cost tradeoffs
Role-specific screening criteria agreed during intake
A framework checklist does not tell you whether someone can own your feature. We document what the candidate has demonstrated, what remains uncertain and which follow-up would help your team decide.
FAQ
Frequently asked questions
Get started
Tell us about your open role
Share your LLM engineer brief. We agree the assessment criteria, budget, launch date and 60-day search scope before you commit.
$2,000 to launch
Fully credited toward the 18% Standard fee
90-day guarantee
One replacement search under agreed conditions
Human review
Recruiters review the screening evidence
Also on Recruo
Roles we hire for
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Compared to other agencies
Further reading
Hire in the UK