MLOps and Platform Engineer

01What the role is

MLOps is the discipline that takes a model out of a notebook and keeps it running in the system that processes real data, returns real predictions, and breaks at 2am when nobody is watching. The title actually covers three related jobs: ML platform engineers who build internal ML platforms, ML infrastructure engineers who live in Kubernetes and the cloud, and applied MLOps engineers who handle model deployment and monitoring.¹ That is why the salary range is so wide.

02What they do day to day

Model versioning and CI/CD for models, building and running inference services, optimizing GPU and inference cost, and standing up the internal platform, think Kubeflow, MLflow, and cloud ML stacks, that lets data scientists and ML engineers ship without reinventing infrastructure each time.¹ The LLM wave landed squarely on this desk: every company that shipped a model wrapper now needs someone to manage prompt pipelines, fine-tuning workflows, and inference spend.

03What it pays

Base pay ranges from $90,000 to $257,000, with national averages of $130,000 to $165,000, and senior engineers at top companies past $300,000 total comp.¹ San Francisco leads on total comp near $215,000, followed by New York, Seattle, Boston, and Austin. Compensation for ML and MLOps roles jumped roughly 20 percent year over year through 2025.¹

The common hiring mistake is budgeting a DevOps salary and expecting MLOps output. At $120,000 in 2026 the role sits open; the people who can ship models to production and keep them running know what they are worth.¹

04Why this role matters to data centers

This is the role that sits closest to the data center itself. ML platform and infrastructure engineers are the bridge between the physical GPUs a facility houses and the models that run on them. Inference-cost optimization, GPU scheduling, and cluster efficiency are exactly the levers that decide whether a data center full of expensive hardware is used well or wasted. As AI campuses scale, this talent becomes as strategic as the power and cooling that feed the racks.

05How to hire one

Decide which of the three sub-roles you need, platform, infrastructure, or applied, before you set the band, because they price and interview differently. Then reach the people with real production and LLM-deployment experience, who are rarely applying. That targeting is what our network is built for.