Screened for how they make AI work in production, not just in a demo.
Why STACK IT
Most agencies optimize for volume. We optimize for the one hire who’s right, vetted by people who understand the work.
We screen for how a candidate builds, evaluates, and deploys real ML and AI systems, not how many model names they can drop. The field moves fast and is full of hype, so a second recruiter signs off before anyone reaches you.
Fake profiles, proxy interviews, and AI-assisted answers are everywhere in tech hiring, and doubly so in AI roles. We meet each candidate face-to-face on video and screen for AI patterns, so who you interview is who shows up.
AI talent is scarce and heavily courted. We align trajectory, growth, and total comp so your hire doesn’t treat your offer as a stepping stone.
Success-based and non-exclusive, no upfront fees, no retainers. We invoice on your hire’s first day, not before.
The payoff
Hiring well costs less than you think, and a strong hire changes far more than the work in front of them.
AI that actually ships.
Real engineering takes the prototype to a reliable system.
Models that hold up on real data.
Proper evaluation and monitoring catch drift and failure.
AI features you can trust with users.
Grounding, guardrails, and evals make output dependable.
AI tied to a real outcome.
An engineer who picks the problems AI can actually solve.
AI systems the team can own.
MLOps and documentation make it maintainable.
How we screen
We screen for how AI engineers actually think. Every shortlist is judged against the same five criteria that predict whether someone delivers in your codebase.
Builds, trains, and tunes models that work on real data, and knows when a simpler approach beats a fashionable one.
Takes a model from notebook to a deployed, monitored, reproducible system, not a one-off script.
Builds RAG, fine-tuning, and LLM pipelines with grounding and guardrails, not just clever prompts.
Measures quality honestly, watches for drift and bias, and can say when a model isn’t good enough to ship.
Strong enough as a software and data engineer that the AI is built on solid ground, not duct tape.
Proof it works
Discover what changed once the right hire joined our clients’ team.
A SaaS company was paying a team to manually triage every incoming document. The engineer we placed built and deployed a classification model with proper evaluation, cutting manual review by 70% while keeping accuracy above the human baseline.
Replaced a fintech’s rules-based fraud screen with a monitored ML model, lifting caught fraud from 74% to 92% with fewer false positives.
Took a healthcare provider’s stalled GenAI pilot to production, with retrieval grounding and evaluation that made the answers safe to trust.
Real technical screening, a calibrated shortlist in days, and candidates vetted for fit, not just resumes. Let’s start your search.
Specializations
Whatever your team runs on, we screen for the people who do the work right.
Training and tuning models that work on real data.
Grounded LLM systems, not just clever prompts.
The pipelines and features the models actually depend on.
Models that ship to production and stay healthy under real traffic.
Evals, guardrails, and red-teaming so models behave in the wild.
Grounded answers from your own data with vector search and retrieval.
The cost of waiting
An empty seat doesn’t delay work, it redistributes it. The longer the search drags, the more it costs.
Every week a role stays open, the cost lands on the team you already have.
Speed isn’t a nice-to-have. It’s the difference between a gap and a setback.
Time to fill this role
How you hire
Two models, one standard of quality. Bring on the AI engineers you need the way that fits your timeline and budget.
Permanent
Best when you’re building the team for the long term.
Contract
Best when you need delivery capacity now, without adding headcount.
Not sure which fits? Compare permanent vs. contract
FAQ
The questions teams usually ask before starting a search with us.
We look past notebook demos to what a candidate has shipped. We walk through AI or ML systems they put into production, probe how they handled data pipelines, evaluation, model monitoring, and failure modes, and have them explain the tradeoffs in plain terms. Building a model in a notebook and running one reliably in production are different skills, and we screen for the second.
An AI engineer builds and ships AI systems into production, with the engineering that entails, while a data scientist focuses on modeling, experimentation, and insight. We match to whichever the role actually needs.
Both. Proofs of concept and specific builds can fit contract, while owning AI systems in production long term usually calls for a permanent hire. We advise based on your stage.
AI and machine-learning talent is among the most competitive in the 2026 market, so bands move fast and strong people field multiple offers. We benchmark against recent placements and move quickly.
Permanent hires are success-based: you pay only when someone starts, with no upfront fee, backed by our 90-day guarantee. Contract runs on a transparent hourly rate. We will walk you through the specifics on an intro call.
Still have a question? Talk to a recruiter
Bill 190 compliant by default.
Every search keeps your hiring audit-ready in Ontario.
Start a search
Share the role and we’ll reply within one business day with a calibrated shortlist of three to five AI engineers, screened for your stack and your team.
