Hire developers · AI & Cloud

AI & Cloud

Hire AI engineers

LLM and agent features built on your own data and permissions, with the evaluation and guardrails designed in rather than added after.

Category
AI & Cloud
Roles
4
Screening
By an engineer, with written evidence
Terms
Quoted per role, in writing

What they are hired to build

The work, before the CV.

A role written against the work gets a better shortlist than a role written against a stack. These are what AI engineer engineers are usually brought in to do.

Roles we recruit: AI engineer · Senior AI engineer · LLM application engineer · GenAI engineer.

  • Retrieval over your own documents, on your existing permissions model
  • Agents inside an operational process, with human checkpoints
  • Prompt and context engineering, and the evals that keep it honest
  • Cutting the token bill on a feature that already works

The technical screen

What the conversation actually covers.

Every candidate has a real technical conversation with an engineer who has done the job, and you get written evidence per competency. In AI engineer, that conversation covers the following.

Usually assessed alongside: Python, TypeScript, LangChain, Vector databases, Evals.

  • Retrieval quality: chunking, ranking, and how they measured it
  • Evaluation — what a regression looks like when outputs are not deterministic
  • Prompt injection and data boundaries, on a system with real permissions
  • Cost and latency per request, and where they cached
Worth saying up front

This is the stack where titles have drifted furthest from the work. We screen for what someone has actually shipped and evaluated, not for how many model names are on the CV.