MLOps Engineer

Axial Search is a specialist executive search firm focused on placing leaders who help organizations navigate AI transformation. We track over 2,000 US postings for MLOps-focused roles annually, with demand concentrated in technology, financial services, and larger healthcare systems. Mid-senior base compensation typically ranges from $140K–$220K.

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

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