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What the market looks like

MLOps and AI infrastructure hiring has picked up sharply as AI programs move past pilot into production at scale. We track over 2,000 US postings for MLOps-focused roles annually, concentrated across technology, financial services, and larger healthcare systems. Mid-senior base compensation generally sits in a $140K–$220K band, and the strongest MLOps engineers we place are those who bring platform-engineering rigour to what has historically been a lower-discipline space.

Job responsibilities

Design and operate the infrastructure that moves models from training to production at scale
Build and maintain CI/CD pipelines for ML — training, evaluation, registration, and deployment
Own observability for production models — latency, drift, data quality, and cost
Partner with ML engineers to codify reproducible training and evaluation workflows
Select, integrate, and evolve MLOps tooling (MLflow, Kubeflow, feature stores) for the team's actual needs
Drive cost and reliability improvements for live inference systems as usage scales
Collaborate with platform and security engineers on cluster, identity, and secrets management for ML workloads
Document standards, patterns, and runbooks so the ML organization can operate without constant hand-holding

Candidate requirements

4+ years in platform, infrastructure, or DevOps with direct exposure to ML workloads
Strong experience with Kubernetes, Docker, and infrastructure-as-code (Terraform, Pulumi, or equivalent)
Hands-on experience with at least one major cloud ML stack (SageMaker, Vertex AI, or Azure ML)
Comfort with Python and shell scripting, with familiarity with PyTorch or TensorFlow
Experience designing observability and rollout strategies for live ML systems
Strong debugging instincts across both infrastructure and model code

MLOps Engineer