Hire AI developers and AI engineers for commerce.
Hire an AI developer who has shipped AI to a live storefront, not to a notebook. Our AI engineers for hire arrive with production RAG, evaluation harnesses, guardrails and cost budgets — and they already understand catalogs, orders and support data.
The scarce skill in AI hiring is not prompt writing. It is the discipline that turns a working demo into a feature you can leave running: a retrieval layer over messy product data, an evaluation set that tells you whether last night's change helped, guardrails that make a wrong answer impossible rather than unlikely, and a cost per interaction that finance will sign off.
That is what our AI engineers do. They have shipped shopping assistants, semantic search, tier-1 support deflection and catalog content operations into production stores, and they know the commerce data models underneath — variants, attributes, price lists, stock, fulfilment states. Hiring a generalist ML engineer into a commerce team usually costs a quarter of learning that.
The work sits alongside our AI for e-commerce practice, so an embedded engineer has an eval harness, a guardrail library and reviewers behind them rather than starting from an empty repository. Engagements are monthly per role, with a paid two-week trial sprint. Rates sit well below UK, US and Australian agency day rates and above the local offshore floor — we are not the cheapest option and do not try to be. We quote per role on the first call, in writing, with no ramp-up fee.
What our AI developers for hire actually do.
Retrieval over real catalogs
Hybrid lexical and vector retrieval over product data, help content and order records — chunking, ranking and freshness handled for data that changes hourly.
Evaluation harnesses
Golden sets built from your own queries and tickets, scored automatically on every change, wired into CI as a release gate. The difference between engineering and guessing.
Guardrails and safety
Constrained outputs, refusal paths, PII handling, and hard limits on price, stock and policy claims. Sensitive intents route to humans by design.
Cost and latency engineering
Model routing, caching, token ceilings and small models for the cheap work — a target cost per interaction set before launch and tracked daily.
Commerce integration
Shipping features into Magento, Shopify Plus and Medusa, and into Zendesk, Gorgias or Intercom — API surfaces, webhooks, auth and fallback behaviour.
Model-agnostic builds
Claude, GPT or open models behind an interface, so you can switch on price, latency or data residency without rewriting the feature.
How hiring an AI engineer works.
Brief in one call
The use case, the data you hold and the number you want moved. We will tell you if AI is the wrong tool — sometimes the answer is better search or a fixed workflow.
Interview named engineers
The specific people who would join, with the production systems they built. Technical screens on your terms, including a look at their eval work.
Paid two-week trial sprint
A scoped slice with a measurable outcome — usually an eval set plus a first retrieval pass, so you can see the quality of the thinking, not just the code.
Embed and run
Your repo, your cloud, your data boundaries. Monthly invoice, 30 days notice, and access to our eval and guardrail tooling for the duration.
The commercials, without a discovery call.
The stack.
Common questions.
Can we hire an AI engineer part-time, or alongside our own team?
Yes to both. A half-time AI engineer is a sensible first step when the work is one feature rather than a programme, and embedding one alongside your own developers is the most common arrangement — they own the retrieval, eval and guardrail layer while your team owns the product surface it plugs into.
What is the difference between an AI engineer and an ML engineer here?
Our AI engineers build systems around existing models — retrieval, evaluation, guardrails, cost control, integration. They do not train foundation models, and for most commerce problems nobody should. If your problem genuinely needs bespoke model training, say so early and we will tell you honestly whether we are the right supplier.
Can an AI developer work on our data without it leaving our environment?
Yes. Retrieval runs against your own systems, and we settle data residency, retention and model-provider terms in writing before work starts. Your data is not used to train third-party models.
What does it cost to hire an AI developer here?
Rates sit well below UK, US and Australian agency day rates and above the local offshore floor — we are not the cheapest option and do not try to be. We quote per role on the first call, in writing, with no ramp-up fee. Note that model and infrastructure spend is separate and billed to your own accounts, so you see it directly rather than through a margin.
How do we know the AI they ship actually works?
Because the first deliverable is the measurement, not the feature. An evaluation set built from your data, scored automatically, is what makes every later claim checkable — including ours. If a supplier cannot show you their eval harness, treat the demo with suspicion.
Would we be better off with a fixed-scope AI project?
Often, for a first build. A defined outcome like an AI shopping assistant or an e-commerce chatbot is easier to buy as a project with us owning the risk. Hire engineers when you intend to own the capability in-house long term.
Where to next.
Ask for the profiles.
Tell us the use case and the data you hold. We will send CVs for the AI engineers who fit — and tell you first if the problem does not need AI at all.