Hiring · 1 minute read
How to Hire MLOps Engineers
To hire an MLOps engineer, verify skills in deploying, monitoring, and maintaining ML models in production—CI/CD for models, versioning, observability, retraining, and infrastructure. MLOps is what turns models that work in a notebook into models that work reliably in production, where most projects fail. Look for a track record of models running dependably in production, not just experiments.
Most models never reach production—or break silently once there. MLOps engineers close that gap. Here's how to hire MLOps engineers.
What an MLOps engineer does
An MLOps engineer deploys, monitors, and maintains machine learning models in production:
- CI/CD for models — automated, repeatable deployment.
- Monitoring — catch drift and degradation.
- Versioning — models, data, and code.
- Retraining — keep models current.
This is what turns a model that works in a notebook into one that works reliably in production—where most AI projects fail.
What to verify
| Verify | Signal |
|---|---|
| Deployment | CI/CD for models, not manual |
| Monitoring | Catches drift and failures |
| Versioning | Reproducible, auditable |
| Production track record | Models running dependably |
MLOps or data science?
Both, at different stages. Data science builds models; MLOps ships and maintains them. Many teams have models that work in notebooks but fail in production—MLOps is the missing capability. See AI team structure.
How to hire
| Option | Best when |
|---|---|
| In-house | Permanent platform team |
| Staff augmentation | Add capacity |
| Delivery partner | Build production ML for you |
Strong MLOps talent is available cost-effectively offshore with US-hours coverage.
Why FISTA
FISTA Solutions provides MLOps that keeps models reliable in production—deployment, monitoring, and retraining—via staff augmentation or delivery, backed by a verified 99.9% uptime record.
Getting models to production reliably? Talk to FISTA.
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Questions raised by this field note.
Straightforward guidance for evaluating scope, fit, and the next step.
01What does an MLOps engineer do?
Deploys, monitors, and maintains machine learning models in production—building CI/CD for models, versioning, observability, retraining pipelines, and infrastructure. They keep models running reliably after the data scientist's work.
02What should I look for when hiring an MLOps engineer?
Deployment and infrastructure skills, monitoring and observability, model versioning and retraining, and a track record of models running dependably in production—not just proofs of concept that never shipped.
03Do I need MLOps or data science?
Both, at different stages. Data science builds models; MLOps ships and maintains them. Many teams have models that work in notebooks but fail in production—MLOps is the missing capability that closes that gap.
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