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What the market looks like
We've tracked 25 senior-level MLOps postings across the US in the last six months, with strong concentrations in California, Texas, and New York. Organizations across technology, financial services, healthcare, and manufacturing are building or scaling MLOps functions to operationalize AI and ML systems at scale. Compensation for director-level MLOps leaders typically ranges from $200K to $330K annually. The strongest candidates bring hands-on experience deploying and maintaining production ML systems, a track record of building or scaling teams, and the ability to bridge data science and infrastructure—combining deep technical judgment with organizational leadership.
Job responsibilities
Own the design, deployment, and reliability of ML infrastructure and CI/CD pipelines that enable data science and AI teams to move faster and more safely
Build and lead an MLOps team, hiring for technical depth and establishing practices that scale with the organization's AI footprint
Partner with data science, engineering, and product leaders to define standards for model versioning, experiment tracking, monitoring, and observability
Drive adoption of MLOps tooling and best practices across the organization, from model development through production deployment
Establish SLOs, alerting, and incident response protocols to keep ML systems performant and reliable in production
Architect solutions for model governance, feature management, and data pipeline orchestration that balance velocity with risk
Work cross-functionally to remove technical blockers, streamline collaboration between teams, and measure MLOps maturity against business outcomes
Candidate requirements
8+ years in software engineering, infrastructure, or data engineering roles, with at least 3-4 years directly building or scaling MLOps practices and tooling
Hands-on expertise deploying and maintaining ML systems in production, including experience with ML frameworks, containerization, orchestration (Kubernetes, Airflow, etc.), and monitoring
Track record hiring, mentoring, and scaling technical teams; experience managing a team of 3+ engineers
Strong understanding of data engineering, model lifecycle management, and the operational challenges unique to ML systems at scale
Demonstrated ability to communicate technical complexity to non-technical stakeholders and align MLOps strategy with business priorities
Experience working in fast-moving environments where you've had to prioritize ruthlessly, ship incrementally, and adapt quickly to changing requirements