Building a demo on a foundation model takes a weekend. Making it accurate, affordable and safe for real users is the job. We recruit the engineers who have done that part.
Start a searchMany of the best applied AI engineers are strong product or backend engineers who moved into LLM work in the last few years. We look for evidence of shipped AI features and evaluation discipline, not just a list of model names on a CV.
We turn the hiring manager's real bar into a behavioural scorecard: what great looks like in the first 90 days, which trade-offs matter, and which signals on a CV are noise.
AI tooling maps the market, including engineers who are not on job boards and people whose titles do not match what they actually build.
Every candidate who is advanced or rejected is reviewed by a person. Shortlists come with written reasoning you can audit, not a black-box score.
Structured interviews and behavioural assessment test judgment, ownership and how someone ships under ambiguity, which is what separates strong AI engineers from strong interviewers.
An applied AI engineer builds products on top of foundation models: retrieval systems, agents, copilots and automated workflows. The core skills are software engineering, evaluation and making sensible trade-offs between quality, speed and cost.
If you are training or fine-tuning your own models and running ML infrastructure, you likely need an ML engineer. If you are building features on top of existing foundation models, an applied AI engineer is usually the better fit. We help you calibrate this before the search starts.
Yes. We recruit across agentic AI and AI-native startups, as well as established companies adding AI to existing products.
Tell us the role and where the pipeline is stuck. You'll hear back within one business day with a scope, a timeline, and a fee.
Start a search