AI-native engineering hire
Hire agentic AI developers to build reliable tool-using workflows.
Find engineers who can move a multi-step AI task into production with clear permissions, recovery paths and measures of success. 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 agentic AI developer searches; the technical bar is agreed in your brief.
- Compensation guide
- Indicative planning band: €65–110K 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
Define the workflow before choosing the agent specialist
An agentic AI developer owns the orchestration around a model: which tools it can use, how state is retained, how progress is checked and when a person takes over. The role is relevant when your product needs to act across systems or complete tasks that involve several decisions. The hiring brief should describe the task and permitted actions before naming a framework.
Some tasks fit a predefined workflow; others need model-directed decisions about the next step. Anthropic’s guide to effective agents explains this distinction and the tradeoffs in complexity, latency and cost. In a hiring discussion, ask the candidate to explain which approach fits your task and how they would test that choice.
The important engineering questions sit around the happy path. What happens if a tool succeeds but its response is lost? Which actions require approval? How are retries prevented from creating duplicate changes? Can an interrupted task resume, and how is an unsuccessful task handed to a person? These are useful screening questions even when the implementation uses an established SDK.
Bring to the intake: the workflow, connected systems, permitted actions, examples of success and failure, expected task duration and the human escalation path. An LLM engineer may cover a contained feature. A specialist becomes more useful when orchestration, persistent state or action control needs sustained ownership.
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 agentic AI developer
Role scope: Use this scorecard to assess whether a candidate can operate the workflow in your brief and keep consequential actions under the agreed controls.
- Workflow design: chooses an appropriate degree of model-directed decision-making for the task.
- Tool control: defines permissions, validates inputs and identifies actions that need human approval.
- Recovery: explains retries, duplicate-action prevention, state persistence and interrupted-task handling.
- Evaluation: measures completed tasks alongside errors, cost and required human intervention.
- Observability: can use a redacted trace to explain a failure and propose a test for the fix.
How we source
How we assess production agent work
We source across applied AI, backend engineering and workflow automation backgrounds. Relevant experience can come from a production agent, a durable workflow service or a tool integration with comparable constraints. An “agentic” job title and a popular open-source project are not prerequisites.
The technical screen follows one task through its execution. We ask the candidate to explain tool boundaries, state transitions, error handling and the evidence used to judge completion. A practical follow-up might introduce a timeout after an external write and ask how the workflow should recover. AI supports the interview; a human recruiter reviews the technical evidence and any gaps before the shortlist reaches you.
A redacted trace, architecture walkthrough or agreed work sample can demonstrate those decisions. Candidates do not need to expose customer data or production access. The shortlist separates model-related experience from backend and operational strengths so your team can assess the combination it actually needs.
Role-specific screening
What we assess
Agent work combines model behaviour with ordinary software responsibilities. We assess tool and workflow reliability as closely as prompt design.
Control boundaries
Permissions, approval and escalation
Role-specific screening criteria agreed during intake
Execution reliability
State, retries and recovery
Role-specific screening criteria agreed during intake
Task evaluation
Completion quality, cost and intervention
Role-specific screening criteria agreed during intake
Framework experience helps when it matches your implementation. The stronger signal is whether the candidate can explain what happens when the workflow leaves the expected path and demonstrate how they would investigate it.
FAQ
Frequently asked questions
Get started
Tell us about your open role
Share your agentic AI developer 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
Hire by location
Compared to other agencies
Further reading
Hire in the UK