Specialty
MLOps Engineering
The role most companies hire six months too late.
Market reality
Budget in the $170K-$230K range for mid-level and $235K-$325K for senior, based on current market data. In our experience, clean, well-scoped searches close in four to seven weeks, while mis-scoped ones tend to drag past ninety days.
The insight that wins this deal
There is a significant gap between companies that can train a model and companies that can serve one reliably at scale. Most MLOps job descriptions are written by hiring managers who think MLOps is a senior data scientist who knows Kubernetes. It isn't. The closest analog is platform SRE for ML systems. The strongest MLOps engineers came up through SRE, DevOps, or data engineering and layered ML platform tooling on top, not through data science.
What we vet for
Model registry and serving infrastructure. Feature pipelines. CI/CD from notebook to endpoint. Model quantization and inference optimization. MLflow, Kubeflow, Ray. And whether they've carried the pager when an inference pod died at 4am.
If you have data scientists on payroll and nothing in production, you don't need another data scientist. You need the role you haven't hired yet.
Delivery
Where this role can sit
US
All three engagement models.
LATAM
Contract.
Pakistan
Contract. Strong depth in the underlying infrastructure skillset.
Next steps