Hire AI / ML engineers

AI talent is the noisiest hiring market in software right now. Every resume mentions LLMs, and the gap between someone who has run a notebook demo and someone who has shipped an AI feature that behaves reliably in front of paying customers is enormous. turnkey.dev vets for the second kind: every AI / ML engineer in the network has put models or LLM systems into production before you ever see them.

What an AI / ML engineer actually does for you

A strong AI / ML hire will:

• Scope the problem honestly. Decide whether the job calls for an LLM API, a fine-tuned model, classical ML, or no ML at all, and be able to defend that call with your data.
• Build LLM features that hold up: retrieval-augmented generation over your own data, structured outputs, tool use, and guardrails against hallucination and prompt injection.
• Set up evaluation before scaling. Test sets, automated evals, and human review loops, so quality is measured rather than vibed, and regressions are caught before users see them.
• Ship to production: serving with sensible latency and cost, caching, fallbacks between providers, and monitoring for drift and failure modes.
• Control spend. Model choice, prompt and context design, and caching decisions routinely swing inference costs by an order of magnitude.

When to hire an AI / ML engineer

The common triggers: you want an AI feature in your product (assistant, search, extraction, summarization) and need it to be reliable, your team prototyped something with an LLM API and cannot get it past demo quality, you have data that should be driving predictions and is not, or an ML system built by a departed hire needs an owner. If the blocker is that your data is scattered and unreliable, start with the data engineering hub below; models are only as good as the pipelines feeding them.

How turnkey.dev vetting works

Every engineer goes through a screen for fundamentals (ML concepts, Python depth, and LLM system design), a practical exercise built around a realistic product problem with an evaluation component, and a review of deployed work: what shipped, how quality was measured, and what it cost to run. Course certificates and demo repos do not pass on their own. We reject far more than we accept.

Seniority, and what each level is for

Most product teams need one senior engineer who can own the feature end to end, not a research scientist. If your problem genuinely needs research depth, we will say so rather than mis-sell.

What it costs and how fast

Vetted AI / ML engineers typically bill in the $90 to $160 per hour range, reflecting the current market for proven production experience. You will see the rate before committing, and requesting a shortlist is free. Expect a shortlist in 2 to 5 days.

Start with a request, not a contract

Tell us the use case, your data situation, the stack, and your timeline. We come back with a short list of vetted engineers who fit, including rate and availability. You interview, run a paid trial if you want, and only then decide. If the fit is wrong in the first two weeks, we re-match at no cost.