Hire developers · AI & Cloud
AI & Cloud
Hire MLOps engineers
The path from a trained model to one running in production — versioned, monitored, and rollback-able when it drifts.
What they are hired to build
The work, before the CV.
A role written against the work gets a better shortlist than a role written against a stack. These are what MLOps engineer engineers are usually brought in to do.
Roles we recruit: MLOps engineer · ML platform engineer · Senior MLOps engineer · ML infrastructure engineer.
- Training and deployment pipelines for models already proven offline
- Feature stores, model registries and reproducible training runs
- Monitoring for drift, latency and cost once a model is live
- Getting a model out of a notebook and behind an SLA
The technical screen
What the conversation actually covers.
Every candidate has a real technical conversation with an engineer who has done the job, and you get written evidence per competency. In MLOps engineer, that conversation covers the following.
Usually assessed alongside: Python, Docker, Kubernetes, MLflow, Terraform.
- Reproducibility: what it takes to retrain last quarter's model exactly
- Rollback — how a bad model is detected and reverted, and how fast
- What they monitor after deploy, and what alert actually pages someone
- Inference cost and latency, and the trade they made against accuracy
Three ways to bring one on
The route changes; the screening does not.
The same three routes apply whichever stack you are hiring for. Which one fits depends on how long you need the person and whether the headcount is approved.
Permanent placement
Companies hiring engineering and data roles
One fee on joining, with a 90-day replacement guarantee.
- Typical timeline
- 3–8 weeks to offer
Contract staffing & staff augmentation
Companies with budget but no headcount
We employ them, you direct them. Monthly, for when the need is real but headcount is not approved.
- Typical timeline
- Monthly, 3-month minimum
Contract to hire
Companies hiring into a new or unclear role
Three to six months as a contractor, then convert. The honest option when neither side is sure.
- Typical timeline
- 3–6 months to conversion
Or have us build it instead
If the need is the work, not the headcount.
AI & Data
MLOps / LLMOps platform setup
Getting models into production properly, and keeping them there.
AI & Data
LLM cost & performance audit
For a live AI feature whose bill or latency has stopped making sense.
Cloud & DevOps
Kubernetes & container platform
Platform engineering for teams that have outgrown running containers by hand.