Hire AI/ML engineers who can explain why the metric moved

Hire when you already have, or will soon have, an AI feature that needs a permanent owner — someone accountable for the evaluation set, the cost dashboard and what happens when accuracy drifts. A project engagement makes more sense when the work has a defined end date and no ongoing ownership need, or when you're still deciding whether machine learning is the right tool at all. We do both, and we'll tell you honestly which one fits what you've described.

Seniority is the largest factor, then how scarce the specific specialism is — an engineer who can own a production inference path is scarcer than one who can fine-tune a small classifier — then how much of your working day needs covering. We put a written role profile together first so the number you're comparing is specific to the role, not a range off a pricing page, and a shortlist follows once that's agreed.

A marketplace matches you to a freelancer and steps back; the engineer's incentives, tooling and career sit outside the arrangement. Our AI/ML engineers are in-house and salaried, reviewed internally, and someone here is accountable for their growth and for whether they stay on your work. We are not the lowest number you'll be quoted — we compete on the seniority of the person and on how long they stay after they've learned your data and your model's quirks.

They join your board, your repository, your branching rules and your stand-up rather than running a parallel plan. Our team works from Punjab, India, which gives a natural afternoon overlap with the UK and Europe; for US clients engineers shift later so your morning is covered. We agree the specific window before anyone starts.

Everything an engineer writes for you — pipelines, evaluation code, model artefacts — is yours, in your repository, from the first commit. They sign your NDA, work under your access controls, and can be restricted to company-managed devices or VPN-only access where your policy requires it. Your data isn't used to train anything outside your project.

When you genuinely need two specialisms running at once — someone owning retrieval quality while someone else owns serving cost — a single hire will be stretched thin covering both. It's also the wrong move if you don't yet have any AI feature in production and haven't tested whether the idea is feasible against your data; that's discovery work, better scoped as a short project than a permanent hire. We'll say so rather than placing someone into a role that isn't ready yet.

Yes, and we'd push back if you didn't want to. We screen first so you're not sifting CVs, then you run whatever process you use for permanent hires — pairing, code review, a walk through an evaluation set, your call. You can reject anyone without justifying it, and nobody joins your team on our say-so alone.

Our own in-house engineers in Sahibzada Ajit Singh Nagar, Punjab — 45+ of them, with over a decade of combined delivery experience, delivering for clients in 15+ countries. Nothing is subcontracted, and nobody joins your team without you interviewing them first. Once someone is placed, everything they write is committed to your repository from day one, so ownership was never in question.