AI Engineers

Typically 50 to 75% less than a comparable British hire once salary, employer national insurance, pension, recruitment fees and overheads are counted. UK-based AI engineers are part of what we place, so that option is genuinely open. In practice most clients land in Eastern Europe, the Philippines or Latin America once they see the same calibre for materially less, and Eastern Europe fits a British working day almost exactly. We are offering the strongest quality-to-cost ratio we can find, exceptional engineers at a sustainable price, with geography the mechanism rather than the headline.

An AI engineer builds production software around models, most commonly large language models; retrieval pipelines, evaluation harnesses, guardrails and the integration that turns a capable model into a dependable feature. Teams hire one when a prototype needs to become a product, because the gap between a convincing demo and a system that behaves acceptably for real users is the entire job. The role sits on a software engineering floor; without solid API design, data handling and testing instincts, the AI layer has nothing sound to stand on.

Evaluation thinking first, retrieval quality second, and a genuine software engineering floor underneath both; prompt tricks are the least durable skill on the list. A strong AI engineer starts by defining how output quality will be measured, builds the harness, and only then iterates, because without that loop every change is a guess. They understand why retrieval usually matters more than model choice, and reason about cost and latency as budgets rather than surprises. They treat prompts as versioned, tested artefacts.

Give them a failure scenario, not a success story; describe an assistant that hallucinates confidently in production and ask them to walk you through the fix. Strong candidates go to measurement first, what the evaluation set looks like, how the failure was detected, what regression protects against its return. Weak ones go straight to swapping models. Ask how they would decide between improving retrieval and changing prompts, and what evidence would settle it.

Demand outstrips supply badly, and unlike several previous technology waves the work underneath is real; models are shipping inside products across every sector. The caution is about titles rather than the field. The label AI engineer is applied to everyone from research-adjacent specialists to developers who once called an API, so demand for the genuine article coexists with a flood of thin claims. Building on the skill set is worthwhile because the durable parts, evaluation discipline, data plumbing and integration judgement, transfer across model generations.

Yes, intensively, and in this role the question folds in on itself, since the job is partly to know exactly when AI output cannot be trusted. Our AI engineers use assistants to write harness code, generate test cases and explore approaches, and the soundness discipline is the same one they impose on the systems they build; nothing counts until an evaluation says so. During vetting we assess whether candidates use AI tooling well, and we also probe whether they can articulate its current failure modes.

AI engineering candidates get the full CTO-led process, plus scrutiny recruiters cannot provide, because in this field the CV keyword and the actual capability sit furthest apart. The stages are a technical assessment, live pair programming with a senior engineer on realistic problems, a cultural fit screen and a psychometric assessment built specifically for software engineers. We look for production evidence, evaluation habits and the software fundamentals the AI layer stands on, and we test how candidates work with AI tools directly.

You are typically interviewing matched candidates within 7 working days of a requirements call, and developers are often embedded and pushing code within about two weeks. From there it is a rolling monthly contract with 30 days' notice, one monthly fee, and no placement charges. You can scale up or down as the work changes, and you pay nothing to interview.

There is a two-week money-back guarantee, and if you want to continue we replace the developer free of charge. In practice this rarely comes up, because you interview a shortlist of two candidates who have already passed a CTO-led technical assessment and a live pair programming session, so the fit question is largely settled before anyone starts.