OpenAI Developers

Around 50 to 75% less than a like-for-like British hire once salary, employer national insurance, pension, recruitment fees and overheads are counted. A UK-based AI engineer is on the table and we place them. Most teams still choose otherwise on ratio, since Eastern Europe, the Philippines and Latin America deliver the same standard for less. What we are offering is the strongest quality-to-cost ratio we can find, proven LLM builders at a price the business can sustain. Nearly every British company now wants OpenAI integration, while developers with real production experience remain scarce.

The OpenAI platform is a set of hosted models and APIs for language, reasoning, vision and speech that teams call from their own software rather than training models themselves. Teams choose it because it turns capabilities that once needed a research group into an engineering problem, and the models are strong enough that quality depends on how you use them. You are renting intelligence from a third party, so pricing, rate limits and deprecations sit outside your control. If the model is one component among many, the platform is usually the fastest route.

Look for systems judgement first and prompt tricks last. A strong OpenAI developer designs the parts around the model, retrieval, evaluation, fallbacks, cost and latency budgets, because the API call itself is the easy bit. They should have shipped something where model output faced real users, and be able to explain how they measured whether it worked. AI has made syntax recall cheap and judgement expensive, and the discipline of testing non-deterministic software still matters. The differentiator is whether they treat the model as a component to be constrained, not magic to be trusted.

Assess them on a small realistic task, then ask how they would know the feature is safe to ship. Give them a flawed prompt-plus-retrieval setup and ask them to diagnose it, strong candidates reach for evaluation data before they reach for prompt wording. Ask what they would log in production, how they would cap spend, and what happens when the model returns confident nonsense to a paying customer. Ask which failures they have actually seen, because anyone who has shipped LLM features has war stories, and anyone who has none has only built demos.

Demand is high and still broadening, because most companies are earlier in adopting LLM features than the noise suggests. Whether it is worth building on depends on what the model does for you. Features that summarise, extract, classify or draft are proven and cheap to attempt, while fully autonomous agents remain harder than the demos imply. The platform moves fast, which means the durable skill is not knowledge of any one model but the habit of measuring behaviour and swapping components as they improve.

Yes, and for OpenAI work that is close to mandatory, since the person building with models should be fluent in working alongside them. Soundness comes from process, not faith. We assess during vetting whether a candidate uses AI tooling well, meaning they review, test and question generated code rather than pasting it in. AI has made producing plausible code cheap, it has made spotting the wrong abstraction expensive, and that judgement is what we select for. We use AI in our own hiring for screening and matching too.

Vetting is CTO-led rather than recruiter-driven, which matters in a field where a CV full of buzzwords is easy to write. Candidates pass a technical assessment, then a live pair programming session with a senior engineer, where we watch how they reason about model-backed systems under real questioning. A cultural fit screen checks they can work embedded in a British team, and a psychometric assessment we built specifically for software engineers looks at how they think. A two-week money-back guarantee plus free replacement covers a wrong match, and 97% stay beyond two years.

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.