AI Engineering · Executive & Specialist Search
Hire AI Engineers
Engineering for the AI Era. Executive search and specialist recruitment connecting exceptional AI and machine learning engineering talent with the companies building production AI.
Who We Are
HireAIEngineers.com is a specialist recruitment and talent partner focused exclusively on the people who build, train, evaluate and operate AI systems in production.
We work with AI-native startups, foundation model labs, ML platform companies and established businesses building serious AI capability — identifying and attracting the engineers who have taken models from working prototype to reliable, affordable production systems.
From LLM applications, agents and retrieval through to model training, fine-tuning, inference infrastructure and evaluation, we place AI and machine learning specialists across the full stack — from the founding engineer at Seed stage to the Head of AI running a platform at scale.
The title "AI engineer" now covers everything from a weekend prototype to a system serving millions of requests. The distance between the two is where hires succeed or fail, and knowing how to tell them apart is the whole job. We assess every candidate on what they shipped, what broke, and how they knew it was working — before they reach you.
We support hiring across the US and internationally, covering roles such as AI Engineers, Machine Learning Engineers, ML Infrastructure & LLMOps Engineers, Research Engineers, Evaluation Engineers, Data Engineers for AI, Forward Deployed Engineers, and technical leadership through to VP Engineering, Head of AI, Chief AI Officer and CTO.
As model capability moves faster than the talent market can train people, the competition for engineers who have genuinely operated AI in production is intensifying. At HireAIEngineers.com we build the specialist teams that take AI from demo to dependable. Make our network your advantage.
HireAIEngineers.com. Engineering for the AI Era.
Market Growth
AI Engineering Demand
Organizational AI adoption has reached 88%, and global corporate AI investment passed $581 billion in a single year. But adoption is not the same as production, and capital is not the same as capability. Behind every deployed AI system is an engineering problem that only specialists can solve — and the supply of engineers who have solved it before has never been tighter.
88%
Organizational adoption of AI has reached 88%, with generative AI hitting 53% population adoption inside three years — faster than the PC or the internet. Nearly every company now has an AI initiative. Far fewer have an AI system their customers rely on.
Stanford AI Index 2026
$581.7Bn
Global corporate AI investment reached $581.7 billion in 2025, more than double the prior year, with $285.9 billion of that in the US alone. The capital has arrived. The engineers who can convert it into working product are the constraint.
Stanford AI Index 2026
89%
The number of AI researchers relocating to the US has fallen 89% since 2017, including an 80% drop in the last year alone. The domestic talent pool is no longer being topped up by inbound migration at anything like the previous rate — which makes the engineers already here considerably harder to hire.
Stanford AI Index 2026
Driving Excellence
Driving Excellence In AI Engineering
Specialist Recruitment for the AI Engineering Community
At HireAIEngineers.com, we specialize in placing the engineering talent behind the AI systems that actually run in production.
The bottleneck in AI has moved. Eighteen months ago it was model capability; teams were waiting for the frontier to improve. Today the frontier is ahead of most companies' ability to use it, and the constraint is engineering — retrieval that returns the right context, agents that recover from failure, evaluation that catches a regression before a customer does, and inference costs that don't eat the gross margin.
Those are engineering problems, but they are a new kind of engineering problem, because the core component is probabilistic. It doesn't return the same answer twice. It can be confidently wrong. The engineers who have internalized that difference are a small group, and they are in demand from every well-funded company at once.
We connect AI-native startups, foundation model labs and platform companies with the engineers who have been through it — across LLM applications and agentic systems, retrieval and context engineering, evaluation and quality, model training and fine-tuning, and the inference infrastructure underneath all of it.
What good looks like now
- Evaluation harnesses that catch a regression before a customer does
- Retrieval that returns the right context, not just a plausible one
- Agents that recover from failure instead of compounding it
- Inference costs that are engineered down without losing quality
Your Competitive Edge In AI Engineering Talent
Hire AI Engineers
We build the teams that take AI from demo to dependable.
AI is no longer a research function sitting beside the product — it is the product, and it is held to the same standards as everything else you ship.
That shift changes what you need to hire for. A model that performs well in a notebook tells you very little about whether a feature will survive real users, real data and a real cost ceiling. The engineers who close that gap are the ones who have built evaluation harnesses, debugged retrieval systems that returned confidently wrong answers, defended an inference bill line by line, and shipped something they could prove was getting better rather than merely different.
There is a second complication specific to this market. AI engineering and machine learning engineering are two distinct talent pools. One builds products on top of foundation models; the other builds and serves models trained on your own data. Companies that need both frequently try to run a single hiring philosophy across the pair, and it doesn't hold.
We partner with founders, CTOs and Heads of AI to scope which pool a role genuinely sits in, map it properly, and deliver a shortlist of engineers who have done the work before.
AI Engineering Recruitment
AI Engineering Recruitment
Hiring an AI engineer isn't a variation on hiring a backend engineer — it's a different assessment entirely.
Every competent developer has now wired a model into something. The resumes look alike, the keywords are identical, and conventional screening lets the prototype builders through while losing the people who have actually operated AI at scale.
We screen on production evidence rather than vocabulary, and we scope every role around what the person will actually build.
01
LLM Application & Agent Engineers
Build the product layer: agentic systems, tool use and orchestration, planning loops, state and memory management, guardrails for irreversible actions, and the integration work that turns model capability into a feature customers rely on.
02
Retrieval & Context Engineers
Own what the model sees and when: chunking and embedding strategy, vector and hybrid search, ranking and re-ranking, citation and grounding, long-context behavior, and knowing when retrieval is the wrong answer entirely.
03
Evaluation & Quality Engineers
Build the discipline that separates shipping from demoing: golden datasets, metrics that match the product need, LLM-as-judge with an understanding of judge bias, regression testing wired into CI, red-teaming.
04
Forward Deployed & AI Product Engineers
Work inside customer environments, close the loop on real usage, and turn model capability into revenue. Includes GTM engineering and the full-stack builders who own an AI feature end to end.
Whether you're making your first AI hire, building out an application team, or appointing the engineer who owns evaluation across the product — we connect you with people who have shipped it before. Engineering Talent for the Production AI Era.
ML & Infrastructure Recruitment
ML & Infrastructure
Production AI doesn't run itself — it takes model and infrastructure specialists.
Below the application layer sits the work that decides whether an AI product is affordable, fast and improvable: the models trained on your own data, the fine-tuning that makes a small model beat a prompted large one, the GPU capacity and serving stack, and the unit economics that determine whether the feature has a margin. This is a separate talent pool from the application layer, and it benchmarks itself against a different set of employers.
01
Machine Learning Engineering & Research
Model training and fine-tuning, parameter-efficient methods and distillation, classical ML and prediction on proprietary data, recommendation and ranking systems, research engineering, and the applied scientists who turn papers into production systems.
02
ML Infrastructure, LLMOps & MLOps
Training and inference infrastructure, GPU clusters and scheduling, serving and model routing, prompt and semantic caching, cost and latency engineering, feature stores, model monitoring, drift detection, pipelines, tracing and observability.
03
Data Engineering for AI
The platforms and pipelines feeding AI systems: ingestion and transformation at scale, labeling and annotation operations, synthetic data generation, data quality, lineage and governance.
Whether you're standing up an ML platform team, bringing inference costs under control, or hiring the leader who decides what you train versus what you buy — we connect you with the specialists who have executed at scale.
How We Work
A shortlist you can decide from, not a pipeline.
We confirm which discipline you actually need — AI engineering, ML engineering, infrastructure, research or data — and tell you on the first call if the role is hirable as written, at the level and package you have in mind.
AI-native startups, foundation model labs, platform companies, big tech and adjacent engineering pools, mapped by discipline rather than by job title.
The engineers you want aren't reading job boards. We reach them with a credible technical conversation about the problem you're solving.
What they shipped, at what scale, how they knew it was working, what it cost and what broke — not just what's on the resume.
Three or four engineers you can make a decision from, each with an honest written read on strengths and trade-offs.
Insights
AI hiring insights
Practical guides and benchmarks for teams hiring AI and machine learning engineers.
Hiring guide
What Is an AI Engineer?
The role defined precisely, how it differs from the five roles it's constantly confused with, and what separates production experience from prototypes.
Hiring guide
AI Engineer vs Machine Learning Engineer
A five-question test for which one your company actually needs, with six real startup scenarios and the cost of getting it wrong.
Interview guide
How to Interview an AI Engineer
A four-stage loop, the specific questions that expose depth, what strong and weak answers sound like, and the red flags that should end a process.
Screening guide
AI Engineer Skills Checklist 2026
Nine skill areas with tick-box checks at mid, senior and staff level, plus what not to screen for.
FAQ
AI hiring, answered
What types of AI roles do you recruit?
AI engineers building LLM applications, agents, retrieval and evaluation; machine learning engineers working on training, fine-tuning and prediction; ML infrastructure, LLMOps and MLOps engineers; research engineers; data engineers supporting AI systems; forward deployed and AI product engineers; and technical leadership through to VP Engineering, Head of AI, Chief AI Officer and CTO.
What's the difference between an AI engineer and an ML engineer?
An AI engineer builds products on top of foundation models — retrieval, agents, evaluation, cost and latency. An ML engineer builds and serves models trained on your own data — feature engineering, training pipelines, model serving. Most teams building LLM products need an AI engineer first; an ML engineer becomes the right hire when you have proprietary data and a prediction problem. See our full comparison.
How do you tell real production experience from prototypes?
Every candidate is assessed on what they built, what broke and how they fixed it — evaluation, scale, cost and reliability — before they reach you. Evaluation is the clearest single signal: an engineer who has never built an eval harness cannot tell you whether your product is getting better or worse, and we'll say so.
How long does an AI engineering search take?
Typically two to three weeks to a shortlist and four to eight weeks to offer. Leadership and founding hires run longer, as do roles requiring a narrow combination of research depth and shipping experience.
Where do you recruit?
Wherever the right engineers are. We work with companies across the US and internationally, on-site, hybrid and remote, and run searches wherever a role genuinely requires it.
What do AI engineers earn?
Compensation has decoupled from general software engineering, particularly at venture-backed companies competing with the frontier labs. Expect a meaningful premium, expect strong candidates to hold multiple offers, and expect equity and problem quality to weigh as heavily as base. We'll benchmark your specific role on the first call.
We're early stage with limited cash. Is it worth talking?
Yes, provided the technical problem and the equity are compelling. We'll give you an honest read on how the role will land in the market before you commit to a process.
Do you recruit executives as well as engineers?
Yes. Retained executive search for C-suite, VP and Head-of roles, and specialist search for senior engineers and technical leads.
Let's Talk
We connect the AI and machine learning engineers who have shipped in production with the companies building the AI era.
Wayne Lindor
PRINCIPAL | PRIZED
HireAIEngineers.com is the AI engineering specialist division of Prized, placing niche engineering talent with the companies building the AI era, across AI Engineering, Robotics & Physical AI, and Liquid Cooling & Data Center Infrastructure. Wayne heads up our AI Engineering practice, which specializes in 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.