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AI Engineer Recruitment
Specialist search for the engineers who build products on top of foundation models: LLM applications, agents, retrieval and evaluation, shipped to production and held to the same standard as everything else you ship.
- LLM applications & agents
- Retrieval & evaluation
- Founding to Head of AI
Production
evidence, not keywords
The discipline
What an AI engineer actually does
An AI engineer is a software engineer who builds production systems on top of foundation models.
They don't train the model. They decide what the model sees, how it's called, what happens when it's wrong, and how you know whether a change made the product better or worse. That means retrieval and context design, agent and tool-use architecture, evaluation, guardrails, and the cost and latency engineering that decides whether the feature has a margin.
It's the newest of the three disciplines we recruit, and the hardest to screen. Every competent developer has now wired a model into something, so the resumes look identical. The difference between someone who built a demo and someone who has run an AI system for real users only shows up when you ask the right questions.
Roles we recruit
AI engineering roles we place
From the first AI hire at a Seed-stage startup to the leader who owns AI across the product.
01
AI / LLM Engineers
Build LLM-powered features end to end: prompts and context, model selection, tool calling, structured outputs, and the integration work that turns a model into something customers rely on.
02
Agent & Orchestration Engineers
Design agentic systems that plan, call tools and recover from failure: state and memory, orchestration, human-in-the-loop controls, and guardrails for actions that can't be undone.
03
Retrieval & Context Engineers
Own what the model sees: chunking and embeddings, vector and hybrid search, re-ranking, grounding and citation, long-context behavior, and knowing when retrieval is the wrong answer.
04
Evaluation & Quality Engineers
Build the measurement that separates shipping from demoing: golden datasets, task-specific metrics, LLM-as-judge with known biases, regression testing in CI and red-teaming.
05
Forward Deployed Engineers
Work inside customer environments to make AI products land: integrations, workflow design, rapid iteration on real usage, and the feedback loop back into the core product.
06
Founding Engineers & Heads of AI
The first AI hire who sets the architecture, evaluation discipline and hiring bar, and the leaders who own AI product strategy and the team that delivers it.
AI Engineering · What we screen for
An engineer who has never built evaluation can't tell you if it's working
How it differs
AI engineering vs ML engineering vs ML infrastructure
Three disciplines that are often confused, often combined in one job description, and hired from three different talent pools.
| AI Engineering | ML Engineering | ML Infrastructure & LLMOps | |
|---|---|---|---|
| What they build | Products on top of foundation models: LLM features, agents, retrieval | Models trained on your own data: ranking, prediction, fine-tuned models | The platform models train and run on: GPUs, serving, pipelines, observability |
| Core skills | Software engineering, context design, RAG, agents, evaluation, cost and latency | Statistics, training pipelines, feature engineering, fine-tuning, model serving | Distributed systems, GPU scheduling, inference optimization, MLOps tooling |
| Typical background | Strong software engineers who moved into LLM products | ML engineers, applied scientists and data scientists who ship | Platform, infrastructure and SRE engineers who specialized in ML |
| Screen for | Evaluation discipline, production failures debugged, cost reasoning | Experiment design, offline vs online metrics, models taken to production | Throughput, latency and cost wins delivered; incidents owned at scale |
| Hire first when | You're building LLM-powered features on foundation models | You have proprietary data and a prediction or ranking problem | Inference cost, latency or reliability is limiting the product |
| Typical titles | AI Engineer, LLM Engineer, Applied AI Engineer, Forward Deployed Engineer | ML Engineer, Applied Scientist, Research Engineer, Recommendation Engineer | ML Infrastructure Engineer, LLMOps / MLOps Engineer, ML Platform Engineer, Inference Engineer |
Discipline
Machine Learning Engineer Recruitment
Model training, fine-tuning, ranking and prediction on proprietary data.
Discipline
ML Infrastructure & LLMOps Recruitment
GPU platforms, inference and serving, pipelines and observability.
Our assessment
How we qualify AI engineers
Every candidate on a shortlist has been assessed on what they actually built, not the vocabulary on their resume. We ask about:
The evaluation they built, and a regression it caught before a customer did
A retrieval or agent failure they debugged in production, and what they changed
How they brought cost or latency down without losing quality
Where they chose not to use a model at all, and why
Signals of real production experience
- Talks in metrics and failure cases, not frameworks
- Has an opinion on when fine-tuning is worth it
- Can explain an inference bill line by line
- Treats prompts and context as versioned, tested code
FAQ
Hiring AI engineers: common questions
What's the difference between an AI engineer and a software engineer?
An AI engineer is a software engineer first, but one who has learned to build with a probabilistic component. They design for outputs that vary, can be confidently wrong and carry real cost per call, and they measure quality with evaluation rather than assuming it.
Should our first AI hire be an AI engineer or an ML engineer?
If you're building features on top of foundation models, it's almost always an AI engineer. An ML engineer becomes the right first hire when you have large volumes of proprietary data and a prediction or ranking problem. Our AI engineer vs ML engineer guide walks through the decision.
How long does it take to hire an AI engineer?
Typically two to three weeks to a shortlist and four to eight weeks to offer. Founding and leadership hires take longer, and so do roles that need a rare mix of research depth and shipping experience.
How do you screen out candidates who have only built prototypes?
We ask about evaluation, production failures, scale and cost before anyone reaches you. Evaluation is the clearest single signal: an engineer who has never built it can't tell you whether your product is getting better or worse.
Do you recruit AI engineering leaders?
Yes. We run retained search for Heads of AI, VPs of Engineering, Chief AI Officers and CTOs, alongside specialist search for senior and staff engineers.
Wayne Lindor
PRINCIPAL | PRIZED
Wayne heads up the AI Engineering practice at Prized, recruiting across LLM applications, agents, retrieval and evaluation, machine learning and research, and the ML infrastructure underneath, up to and including VP Engineering, Head of AI, Chief AI Officer and CTO level.
Hiring an AI engineer? Let's talk
Tell us what you're building and where the gap is. A brief scoping call gives you a read on the market, an honest view of whether the role is hirable as written, and a clear next step.