For AI features that survive contact with real users, not just a demo
Hire AI Engineers
An AI feature that impresses in a demo can still hallucinate, drift or blow through its inference budget once real users touch it. These engineers are tested on catching those exact failure modes. They are then placed to keep your AI features honest once they leave the demo.
- Profiles typically within two business days
- Replacement Assurance: If an engineer is not the right fit, a replacement from our own team starts within ten business days of your request, at no extra cost
- You interview before placement if you want to
- At least four hours a day inside your core working hours. Two hours plus a daily written handover for the US West Coast
- 14-Day Risk-Free Trial: For your first engineer on a time-and-materials engagement. Up to 14 calendar days or 80 logged working hours, whichever comes first. End within the trial and pay nothing for trial hours. Continue beyond it and those hours are billed at the agreed rate. Unpaid trial work remains ours if you stop
How we evaluate an AI engineer
The tasks, the signals and the onboarding, walked through by the people who run them.
90 secSample frame · video pending01Published, not promised
The assessment behind every engineer we send.
The AI engineering bar is published, not assumed: five competencies, the exact task behind each, and the shortcut that ends a candidacy. Most interviews ask about which model someone has used. This one asks whether the feature can be trusted.
What we listen forWhether they can measure quality objectively, not just eyeball a few outputs.
A team ships an LLM feature with no way to know whether a prompt change made it better or worse. Propose an evaluation approach.
- What strong sounds like
- Proposes a concrete evaluation set with measurable criteria before touching the prompt, treating quality as something to test, not sense.
- What gives us pause
- Proposes trying the new prompt on a handful of examples and comparing outputs by eye.
Python · PyTorch · LangChain
OpenAI API · Anthropic API · Vector Databases
Eval Frameworks · A/B Testing · Human Review
MLflow · Weights & Biases · Docker
02Who you actually meet
The bench, priced by seniority.
Three seniority tiers, priced honestly, and the engineers who fill each one today. Real names and photos take their place as soon as we can share them, and the standard stays exactly the same.
Engineer
Features scoped tightly enough to ship inside your review bar, every time.
The right first hire when your seniors already set the standard.
$3,000–3,800per month
Senior
The hard module nobody else wants to own, taken end to end.
Ownership of a surface, without adding a manager to your headcount.
$4,100–5,200per month
Lead
The workstream's decisions, made daily, so nothing waits on a meeting.
One accountable voice for a pod or initiative that can't afford to drift.
$5,500–6,900per month
AI engineer
3 yrs · US East overlap
Built an evaluation set that caught a prompt regression before it reached production
- Python
- LangChain
- OpenAI API
Senior AI engineer
7 yrs · UK + EU hours
Cut an AI feature's inference cost in half without a measurable quality drop
- PyTorch
- Vector Databases
- MLflow
Lead AI engineer
10 yrs · EU + US split
Owns the evaluation and guardrail standard three product teams now build against
- Architecture
- Evals
- Anthropic API
- Observability
Indicative ranges, representative profiles. Exact figures and named engineers arrive with your shortlist.
Weighing this against a freelancer, a direct hire or a marketplace? See the honest comparison
03From stated problem to first change
Four steps, each closing on evidence.
The evaluation below tests judgement about production AI, not familiarity with a specific model's API. Stopping at any point along the way costs you nothing.
Define the reliability problem
We map where your AI feature's evaluation, guardrails or cost are actually unproven. The brief names that gap, not a list of models to know.
A brief your leads signMatch against your system
Candidates are screened against your actual model choices, your data sensitivity and your review culture. Three engineers who understand your system beat thirty who list LLM experience.
Three fits, not thirty CVsAssess on the real tasks
Your team can watch the evaluation-harness and guardrail-design exercises directly. What shows up is engineering judgement, not enthusiasm about a particular model.
Evaluation you can watchOnboard inside your process
Access, standards and a first bounded improvement to a real AI feature. Continuity cover keeps a departure from ever becoming your emergency.
A merged change in week one
04Evidence and proof
Engineers who kept the AI feature honest after launch.
AI proof is quality that holds after launch, not a demo that only worked once. Every record here waits for written client approval before production.
Explore case studies
A support assistant that stopped confidently making things up.
Koru Health's support assistant occasionally answered questions using information that was not in its source documents. An embedded AI engineer traced the gap to a retrieval failure and fixed the grounding before the next release.
- Ungrounded answers
- Eliminated
- Time to root cause
- Under one week
He built an evaluation set that caught a prompt regression before it ever reached users.
Sample · pending client approval
Our inference costs dropped by half without anyone noticing a change in quality.
Sample · pending client approval
05Three shapes, one standard
Add an engineer, an AI pod, or a practice.
Most teams start with one engineer strengthening an existing AI feature. Some need a pod that owns AI reliability across the product. Where building and shipping an AI capability outright matters more than staffing a role, we point you to the practice instead.
- Team extension
One named engineer
A single AI specialist inside your direction and your reviews. The smallest commitment, and the way most engagements begin.
- Delivery pods
An AI pod
A pod with its own lead that owns evaluation, guardrails and cost across every AI feature you ship. You set priorities while the pod carries delivery.
- AI & Automation
The practice, not the person
Where building and shipping an AI capability outright matters more than staffing a role, AI & Automation takes on that delivery instead.
06Straight answers
Asked before an AI engineering hire is signed.
01What does an AI engineer actually do day to day?
Designs evaluation harnesses, builds guardrails against real failure modes, and keeps inference cost and latency proportional to actual value delivered. The unglamorous majority of the work is proving a feature still works after it ships, not building it the first time.
02Do you hire for a specific model provider?
Matched to your actual stack. Evaluation and guardrail judgement transfers across OpenAI, Anthropic and open models, then the evaluation confirms familiarity with whichever you run.
03Can we run our own interviews as well?
Yes, the published tasks are built to be reused directly. Several clients now run the guardrail-design exercise themselves in later hiring rounds.
04How fast can an AI engineer start?
A shortlist arrives within days from a bench evaluated continuously. Real start speed after that tracks how quickly your side can grant system and data access.
05Who manages the engineer day to day?
Your leads set priorities and hold the quality bar. What we carry sits behind the person: continuity, payroll, replacement risk and the handoff work if plans change.
06What if the engineer is not the right fit?
Say so inside the first weeks and the replacement costs you nothing. The next engineer arrives briefed on the specific gap, which keeps a second miss rare.
07Do your AI engineers use AI coding tools themselves?
Yes, for scaffolding code and test cases. The AI systems they build for you are a separate matter entirely, governed by the evaluation and guardrails above, not by how the code itself was drafted.
08How does pricing work for a hired AI engineer?
Pricing follows the tier bands above, with the shortlist fixing the exact number once scope is clear. Billing runs monthly per engineer, with no placement fee and notice-period exit terms.