Vector DB Developers

Expect to pay 50 to 75% less than an equivalent British hire when salary, employer national insurance, pension, recruitment fees and overheads are added up. Hiring a UK-based engineer through us is perfectly possible. The quality-to-cost ratio usually points to Eastern Europe, the Philippines or Latin America, and for a British team Eastern Europe is strongest, an hour ahead of London. Vector databases are an emerging category, and British engineers with hands-on production experience are rare enough that local hiring means senior AI rates. You can be interviewing matched candidates within 7 working days.

A vector database stores embeddings, numerical representations of meaning, and retrieves items by similarity rather than exact match, the mechanism behind semantic search and AI access to your own data. Teams use one because language models do not know your documents, and retrieval fills that gap, while similarity search across millions of items needs purpose-built indexing ordinary databases lacked. Postgres with pgvector now handles modest workloads well, so a dedicated engine is justified by scale, not fashion. The database is the easy third; embedding choice and chunking decide whether users get good answers.

Retrieval quality engineering, above familiarity with any particular engine. The engines share concepts, indexes, filters and hybrid search, and a capable developer moves between Pinecone, Weaviate, Qdrant or pgvector in days, so hire for judgement about embeddings, chunking strategy, combining semantic and keyword signals, and building evaluation sets that prove relevance instead of assuming it. They should reason about cost and latency at scale. AI has made a vector search demo almost free, while a retrieval system users trust remains genuinely hard, and that gap is the skill you are paying for.

Ask one question early and weigh it heavily, how would you prove retrieval quality improved after a change. Strong candidates talk about golden datasets built from real queries and measured relevance; weak ones talk about trying it and seeing. From there, ask when they would choose pgvector over a dedicated engine, and what they would do when a stakeholder reports search feels worse. Give them messy documents and ask for a chunking plan. Since assistants now generate retrieval pipelines readily, have them critique generated code; a good candidate names the subtle failure modes unprompted.

Demand has grown sharply with retrieval-augmented AI products, and it is durable demand, because every organisation wiring language models to private data needs this retrieval layer built well. The category is consolidating and features converge across engines, so bet on the discipline, not the vendor, embeddings, evaluation and hybrid retrieval will outlive whichever database wins. A developer hired for those transfers as tools change; one hired for a single console does not. It is also fine to start small, pgvector inside the database you already run, graduating to a dedicated engine when scale demands it.

Yes, they build with AI daily, which has a pleasing circularity when the work is building the retrieval infrastructure AI products depend on. Soundness in this field has a specific shape, retrieval code rarely crashes, it just quietly returns worse results, so the discipline that matters is measurement, evaluation sets, relevance checks and regression tests on quality, not only on code. We assess whether candidates use AI tooling well during vetting, and the live pairing session with a senior engineer shows whether they verify generated pipelines against real data or trust clean-looking output.

Through CTO-led vetting rather than recruiter screening, which matters in a field new enough that CV claims routinely outrun real experience. Candidates sit a technical assessment covering data engineering and retrieval reasoning, then a live pair programming session with a senior engineer on realistic problems, where evaluation thinking, chunking judgement and cost awareness show themselves. A cultural fit screen and our psychometric assessment for software engineers map how they reason. Matched candidates reach your diary within 7 working days, and embedding completes in about two weeks, backed by a money-back guarantee and free replacement.

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.