We place the engineers building AI into real products, not the ones who added it to their headline last quarter.
Two years ago this title barely existed. Now every resume has it. The gap between an engineer who has shipped a model into production and one who finished a course and built a demo is enormous, and it is invisible on paper.
Hiring managers feel this and overcorrect, usually by demanding a PhD or a FAANG logo. That filter throws out most of the people who can actually do the job, and it puts you in a bidding war for the ones it keeps.
We come from software development staffing, so we screen engineering depth first and treat the AI layer as what it is, engineering with a model in the middle.
The question is not whether someone can fine-tune a model. It is whether they have put one in front of real users, watched it fail, and fixed it. That is what we dig into.
Most AI roles are eighty percent engineering: data pipelines, evaluation, latency, cost, and the plumbing that keeps it running. We screen for that, not just the modeling layer.
Strong AI engineers are off the market in weeks and usually hold multiple offers. We work fast and we know how to close someone who has options.
A working prototype and a system that survives real traffic are different disciplines. The resume language is identical, so the difference only appears months later when nothing scales.
Many teams hire an AI engineer when the real bottleneck is that their data is unusable. The hire spends a year building pipelines and both sides feel misled.
Requiring a PhD or a specific logo narrows the pool to the most expensive and most contested candidates, and excludes applied engineers who have actually shipped.
AI salaries moved faster than most comp bands. Searches built on last year's numbers stall at the offer stage after months of work.
Without a concrete outcome, the role becomes open-ended research. That is expensive, hard to evaluate, and the first thing cut when budgets tighten.
In banking and legal, model governance, explainability, and data handling are not optional. Engineers who have never worked under those constraints struggle, and it does not show up in a standard interview.
Our technology practice is run by people who have spent their careers in IT staffing: fifteen years placing infrastructure, development, and delivery talent for Fortune 500 and mid-market firms, plus nine years in IT services and recruiting. This is not a vertical we added last quarter.
Contract, contract to hire, and direct placement, across W2, corp to corp, and 1099. We have staffed technology every way it gets staffed and will tell you which one fits your problem.
AI and machine learning salaries run well above general software engineering, and recruiter fees are a percentage of first-year salary. Send us the role and the level and we will give you a straight number rather than a range that means nothing.
We ask what they put in production, what broke, and what they did about it. Anyone can describe a model architecture. Far fewer can walk you through an evaluation loop they built and the tradeoffs they made on cost and latency.
Usually not, and requiring one narrows your pool to the most expensive and most contested candidates on the market. For applied work, engineers who have shipped systems often outperform researchers. We will tell you when a role genuinely needs research depth.
Yes. Model evaluation, data pipeline builds, and proof-of-concept work often suit contract engagements better than permanent hires, and we will say so when that is the honest read.
A first AI hire or a full applied team, send us the details and we will move fast without skipping the screen.