Hire an MLOps Engineer
A senior MLOps engineer who turns notebooks and prototypes into reliable, observable, cost-controlled production systems.
What a MLOps Engineer does
- Deploy models and LLM apps on AWS, Azure, GCP, or your tenancy
- Build CI/CD pipelines, evals, and rollback for model changes
- Set up observability — logs, traces, cost dashboards, alerts
- Harden secrets, PII handling, and access boundaries
- Tune cost and latency across providers and model tiers
Outcomes you can expect
- Production-grade deploy with rollback and on-call runbooks
- Per-feature cost visibility and budget alerts
- Repeatable model release process your team can run
When to hire
- Your AI prototype works but you can't safely ship it
- Costs are unpredictable and nobody owns them
- You need SOC 2, HIPAA, or PCI-aligned deployment
FAQ — Hiring a MLOps Engineer
What does an MLOps engineer actually do for an LLM project?
They own everything around the model: deploy targets, CI/CD, secret handling, evals running on every release, observability, cost dashboards, and incident response. They're why a prototype turns into a system you can rely on.
How is MLOps different from DevOps?
MLOps adds model-specific concerns — eval pipelines, prompt and model version pinning, drift and cost monitoring, and PII-aware logging. A pure DevOps engineer usually doesn't have these patterns yet.
Can your MLOps engineer work inside our cloud?
Yes. We work inside your tenancy on AWS, Azure, or GCP, sign BAAs and DPAs, and support SOC 2, HIPAA, PCI, and GDPR-aligned deployments.
Ready to hire an mlops engineer?
Book a 30-minute launch call. We'll confirm fit, share matched candidates, and get the right person started inside two weeks.