Hire MLOps engineers
Senior engineers who industrialise machine learning: reproducible pipelines, reliable deployment, monitoring and retraining that keep models honest in production.
MLOps engineers available, ready to start within weeksWhat an MLOps engineer brings to your team
Models do not fail at training time; they fail quietly in production, when data drifts and nobody owns the pipeline. An MLOps engineer builds the machinery that prevents that: versioned data and models, automated deployment, monitoring with teeth and retraining that triggers on evidence.
Our MLOps engineers come from software and platform backgrounds, so the ML infrastructure they build behaves like infrastructure: tested, observable and documented, including the lineage and logging that regulated industries and the EU AI Act increasingly expect.
- Senior by default: 5+ years of commercial experience and a reviewed work sample.
- You interview every hire; we place no one you have not signed off.
- Your process, your repo and your tools, followed from day one.
What our MLOps engineers build
The work our MLOps engineers ship, from day one.
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Pipeline industrialisation
Turning notebook workflows into versioned, scheduled, reproducible pipelines with tests.
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Deployment platforms
Model serving with rollout, rollback and shadow deployment, so updates are routine instead of risky.
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Monitoring and drift
Dashboards and alerts on data quality, prediction drift and business metrics, wired to retraining triggers.
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Compliance-grade lineage
The versioning and decision logs that let you reconstruct any prediction, as auditors and the AI Act expect.
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Cost engineering
Right-sizing training and inference infrastructure, often the fastest saving in a mature ML estate.
A simple process, measured in days
- 1 Step 01
Discovery call
The role, your stack and your working norms, in 30 minutes.
- 2 Step 02
Shortlist in days
Senior candidates with work samples and references.
- 3 Step 03
You interview
Every hire clears your bar before joining.
- 4 Step 04
Onboarding
Into your repo and tools, paired on the first sprint.
- 5 Step 05
Scale up or down
A 30-day rolling notice, no minimum term beyond the first month.
Senior engineers ready now, not in a quarter.
We keep vetted people available, so you get a shortlist in days and a start in weeks. A 30-day rolling notice, no long lock-in.
Frequently Asked Questions
How fast can I hire MLOps engineers?
You get a shortlist of vetted candidates within a few days. Most engineers start two to four weeks after the brief.
MLOps engineer or DevOps engineer, which do we need?
If the pain is models (drift, retraining, reproducibility), MLOps. If the pain is general infrastructure, DevOps. The profiles overlap and we match to the actual pain.
Which platforms do they work on?
The major clouds and the common open stack: Kubernetes, MLflow-style registries, Airflow or cloud-native pipelines, plus your feature store of choice.
Can we interview candidates before they start?
Yes. You interview every candidate and decide who joins your team.
Can we scale the team later?
Yes, up or down with a 30-day notice, through staff augmentation for individuals or team augmentation for a whole squad.
Add a senior MLOps engineer to your team
A 30-minute call to scope the role, the seniority and the start date. You interview every hire.