For models that stay reproducible, deployable and watched
Hire MLOps Engineers
A model that was trained once and deployed by hand is a liability waiting for its first retrain. These engineers are tested on making training reproducible, deployment reversible and degradation visible. They are placed inside your platform team to keep a model honest for as long as it serves.
- 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 MLOps 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 MLOps bar is published, not assumed: five competencies, the exact task behind each, and the shortcut that ends a candidacy. Most interviews ask which orchestration tool someone prefers. This one asks whether a model can be rebuilt, replaced and watched without a person remembering the steps, because that manual dependency is the real risk.
What we listen forWhether a model can be rebuilt from its lineage, not from someone's memory.
A model in production cannot be retrained to the same result because data, code and parameters were never versioned together. Propose the pipeline that fixes it.
- What strong sounds like
- Versions data, code and configuration together, makes every training run reproducible from a single identifier, and gates promotion on a validation step.
- What gives us pause
- Proposes better documentation of the manual steps, leaving the run itself unreproducible.
Airflow · Kubeflow · MLflow
Kubernetes · Seldon/KServe · Model registries
Prometheus · Evidently · Drift detection
Docker · Terraform · Feature stores · CI/CD
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.
$4,100–5,100per 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.
$5,400–6,700per month
MLOps engineer
4 yrs · US East overlap
Versioned data, code and parameters together so a model nobody could retrain reproduced from one run identifier
- Python
- MLflow
- Airflow
- Docker
Senior MLOps engineer
7 yrs · UK + EU hours
Built the canary serving path whose automatic rollback caught a worse model before any customer saw it
- Kubernetes
- KServe
- Terraform
- Prometheus
Lead MLOps engineer
10 yrs · EU + US split
Owns the drift-monitoring and cost-attribution standard across a multi-model platform
- Architecture
- Kubeflow
- Feature stores
- Evidently
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 gap to first change
Four steps, each closing on evidence.
This process tests operational judgement about real pipelines, not familiarity with tool names. You can walk away at any step owing nothing.
Define the operational gap
We map where your pipelines, deployments or monitoring are actually fragile. The brief names that gap, not a list of platforms to know.
A brief your leads signMatch against your platform
Candidates are screened against your actual stack, data volume and review culture. Because the bench is small, you see who is available and from when, stated as a confirmed date.
A confirmed date, not a hopeAssess on the real tasks
Your team can watch the reproducibility and rollout exercises directly. What shows up is operational judgement, not familiarity with a specific tool's syntax.
Evaluation you can watchOnboard inside your process
Access, standards and a first bounded improvement to a real pipeline. If the engineer ever leaves, continuity cover means the pipeline never goes unwatched.
A merged change in week one
04Evidence and proof
Engineers who kept the model reproducible, reversible and watched.
MLOps proof is a retrain that matched, a rollout that reversed and a drift alert that fired first. Every record here waits for written client approval before production.
Explore case studies
A model nobody could retrain, rebuilt from a single run identifier.
Vela Ledger's fraud model could not be reproduced after its author left. An embedded MLOps engineer versioned data, code and parameters together, and the next retrain matched the original result from one identifier.
- Retrains reproducible
- Every run, one identifier
- Time to reproducible pipeline
- Two sprints
The canary rollout he set up caught a worse model before a single customer saw it, and rolled back on its own.
Sample · pending client approval
We finally know what our ML platform costs per model, and who owns each line of it.
Sample · pending client approval
05Three shapes, one standard
Add an engineer, a platform pod, or the service.
Most teams start with one engineer making an existing pipeline reproducible. Some need a pod that owns the ML platform end to end. Others want the platform delivered as a whole, which is the MLOps service rather than a hire.
- Team extension
One named engineer
A single MLOps specialist inside your direction and your reviews. The smallest commitment, and how most engagements start.
- Delivery pods
A platform pod
A pod with its own lead that owns pipelines, serving and monitoring end to end. You set priorities while the pod carries delivery.
- MLOps Services
The service, not the person
Where a whole MLOps platform delivered matters more than one hire, the MLOps service takes that scope on instead.
06Straight answers
Asked before an MLOps hire is signed.
01What does an MLOps engineer actually do day to day?
Makes training runs reproducible, builds deployments that can be reversed, and sets up monitoring that catches drift before a customer does. The unglamorous majority of the work is removing the manual step that would otherwise fail at the worst moment.
02How is this different from an ML engineer?
An ML engineer designs features, chooses metrics and builds the model. An MLOps engineer makes that model rebuildable, deployable and observable in production. Many teams need both; the shortlist names which one your gap actually calls for.
03Do you hire for a specific MLOps platform?
Matched to your actual stack. Pipeline, serving and monitoring judgement transfers across Airflow, Kubeflow, MLflow and cloud-native services, then the evaluation confirms familiarity with whichever you run.
04How fast can an MLOps engineer start?
This is a small specialist bench, so we do not promise immediate availability. Your shortlist states each engineer's start as a confirmed date, and that date is a commitment. Real start speed after that tracks how quickly your side can grant platform access.
05Can we run our own interviews as well?
Yes, the published tasks are built to be reused directly. Several clients now run the reproducibility exercise themselves in later hiring rounds.
06Who manages the engineer day to day?
Your platform lead sets priorities and reviews every pipeline and deployment change. The employment side stays with us: payroll, continuity, replacement risk and the handover work when a plan changes shape.
07What if the engineer is not the right fit?
Tell us inside the first weeks and we fund the replacement. The next engineer is briefed on the specific gap, and because start dates on this bench are confirmed dates, you know exactly when they begin.
08How does pricing work for a hired MLOps engineer?
The seniority bands for this specialism set the expectation, and the shortlist confirms the figure per engineer once scope is agreed. Billing is monthly with no placement fee, and exit follows the notice period.