AI Engineers.
Applied AI and machine learning engineers for production roles: ML systems users depend on, LLM application engineering, AI infrastructure, and the evaluation and monitoring work that keeps all of it honest.
You probably do not need a researcher. You need an engineer who ships.
The most common miss in AI hiring is profile confusion: hiring a research profile for a production role. Research skill is real and valuable, but a publication record does not predict the ability to ship a model behind an API, keep it fast and affordable at scale, and take the pager when it degrades. Companies that interview for theory and hire for prestige end up with impressive people and stalled products.
Applied AI engineering in production splits into a few distinct jobs. Production ML engineers own models in serving: training pipelines, deployment, latency, drift, and rollback. LLM application engineers build products on top of foundation models: retrieval, prompt and context engineering, guardrails, and cost control. AI infrastructure engineers build the platform underneath: GPU scheduling, inference serving, vector stores, and the tooling other engineers ship on. And the evaluation and monitoring work, deciding how you know the system is good and catching it when it stops being good, is a first-class engineering discipline of its own, not an afterthought.
The screen we run reflects that. We look for engineers who can explain what they shipped, who used it, what broke, and what it cost, and who talk about evaluation as part of the build rather than a compliance step. This page sits inside a wider practice built around a simple idea: the talent that matters now is the talent that converts AI and data products into outcomes customers can see.
Where the AI engineer must also work directly inside customer environments, the search usually belongs on our Forward Deployed Engineering desk instead; the profiles overlap but the screen is different. And when the blocker is upstream of the model entirely, it is often a data engineering search wearing an AI title. We will tell you which search you actually have at scoping.
Why Platforms Choose Engaged Search
Where the search lives.
Senior leadership and niche-headhunter roles. The recruiter assigned to your search has spent a decade in this segment.
APPLIED AI & ML
INFRASTRUCTURE & EVALUATION
CUSTOMER-FACING AI
AI Engineers, specifically.
The questions hiring operators send in the first call.
What is the difference between an AI engineer and an ML engineer?
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The market uses the titles loosely. In practice, machine learning engineer usually means training, deploying, and operating models; AI engineer increasingly means building products on top of foundation models, where the work is retrieval, context engineering, evaluation, and cost control rather than training from scratch. What matters for the search is defining the work, not the label, and we do that with you at scoping.
How do you avoid hiring a research profile for a production role?
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By interviewing for production evidence. We ask what the candidate shipped, who depended on it, how it was evaluated, what it cost to run, and what they did when it degraded. Candidates with genuine production ownership answer in specifics. Research-only profiles answer in methods. Both are valuable people; only one fits a production seat.
Do you place LLM and generative AI engineers specifically?
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Yes. LLM application engineering is one of the most active corners of the practice: engineers who build retrieval pipelines, design evaluation harnesses, manage guardrails, and keep inference costs under control. The strong ones are employed and quietly fielding offers, which is why these searches lean on direct outreach rather than postings.
What about evaluation and monitoring roles?
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We treat evaluation as its own competency, and increasingly its own role. Companies that put an AI system in front of customers without an evaluation and monitoring owner find out about failures from the customers. Whether it is a dedicated seat or a required skill in a broader role, we screen for it explicitly.
Should an AI engineering search run engaged or contingent?
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Senior and specialized roles usually run engaged: 25 to 35 percent of first-year compensation, with a deposit credited against the final fee, funding the direct outreach a passive market requires. Contingent, at 18 to 25 percent owed only on placement, fits roles closer to the active market. Replacement guarantee terms are agreed in writing at the start of the search, and candidates never pay us anything.
Three paths from here.
The role also has to work inside customer environments.
Read the Forward Deployed Engineers page. When the AI engineer deploys in the customer's stack, the screen changes, and so does the search.
Read forward deployed engineers →The real blocker might be the data underneath the model.
Read the data engineers page. A large share of stalled AI initiatives are data engineering gaps with an AI label on them, and it changes who you should hire first.
Read data engineers →The role is defined and you want candidates who have shipped.
Book a scoping call. We map the role, recommend engaged or contingent, and come back with a fee quote and a candidate market read.
Start the scoping call →Tell us what the model has to do in production.
Tell us what the AI actually has to do in production and who depends on it. We scope the role from there, recommend the engagement model, and give you a straight read on the market.