MLOps Engineer

Axial Search is a specialist executive search firm built for one kind of hire: leaders who help organizations navigate AI transformation. Apply today to express your interest in roles like this one. Visit our website to learn more about our process and explore free tools for your job search, including our live job market dashboard with salary, skills and hiring trend data from thousands of AI transformation roles. 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