Hire Generative AI Engineers

The engineers here have taken LLM features past the demo stage into systems with real users, real permissions and real budgets, which is where most of these projects fail.

A typical week

• Designing retrieval so answers are grounded in your content and cite their sources
• Building agent graphs with explicit state, checkpointing and human escalation paths
• Running evaluation sets on every prompt or model change to catch regressions
• Managing token cost and latency through caching, routing and model selection
• Handling the unglamorous edges: permissions, PII, refusals and audit trails

What we screen for

How we assess this role before anyone reaches your shortlist.

• Do they measure quality with an evaluation harness rather than by trying prompts
• Can they explain when an agent is the wrong architecture for a problem
• How do they handle grounding, citation and refusal when evidence is missing
• Are they deliberate about inference cost and latency, not just output quality
• Have they shipped an LLM feature to real users and dealt with the aftermath

Related specialisms

Frequently asked questions