Senior MLOps Engineer

Role overview

Senior MLOps Engineer (partner-led role) to shape MLOps infrastructure and operating standards for production machine learning at scale. You’ll design and evolve MLOps platforms, workflows, and governance practices across engineering, analytics, and platform teams—improving the end-to-end ML lifecycle from experimentation through deployment and monitoring.

Responsibilities

• Lead the design, development, and improvement of MLOps platforms and processes supporting ML teams
• Develop and standardize workflows for model development, training, deployment, monitoring, and lifecycle management
• Partner with data scientists and engineers to improve ML workflows from experimentation to production operations
• Establish model governance practices (e.g., reproducibility, lineage tracking, access controls, operational standards)
• Support ML tooling such as experiment tracking, model packaging, deployment workflows, and model promotion (e.g., MLflow)
• Onboard and support new ML use cases; improve shared ML capabilities across teams
• Troubleshoot and resolve infrastructure issues (scalability, reliability, performance, and cloud cost optimization)
• Build automation using infrastructure-as-code, CI/CD pipelines, and standardized engineering practices
• Improve observability, monitoring, and operational processes for ML systems
• Align ML systems with broader data architecture and governance strategies (with data engineering/platform teams)
• Create documentation and technical standards to increase consistency and efficiency

Requirements

• 5+ years of experience in MLOps, machine learning engineering, platform engineering, data engineering, or a related field
• Hands-on experience managing production workloads on AWS and strong cloud infrastructure knowledge
• Solid understanding of the machine learning lifecycle (training, deployment, monitoring, maintenance)
• Experience supporting production-grade ML workflows with reliability, scalability, and reproducibility
• Familiarity with ML tooling such as MLflow and/or experiment tracking/model management platforms
• Experience with workflow orchestration, infrastructure-as-code, and CI/CD for ML/data platforms
• Knowledge of secure access patterns, governance controls, and shared cloud services

Nice to have

• Experience improving engineering standards and operational practices across multiple teams