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What the market looks like
We've tracked 16 senior-level MLOps postings across the US in the last six months, with strongest hiring concentration in California, Texas, and New York. The role spans technology, financial services, healthcare, and manufacturing sectors. Compensation typically ranges from $210K to $330K annually. Directors who stand out bring hands-on experience operationalizing ML workflows at scale—they know ML infrastructure deeply, lead cross-functional teams through real deployment challenges, and balance platform reliability with model velocity. The best candidates also demonstrate comfort bridging engineering and data science teams, translating technical constraints into business outcomes.
Job responsibilities
Own the end-to-end MLOps strategy and roadmap, defining standards for model deployment, monitoring, and lifecycle management across the organization
Lead and mentor a team of MLOps engineers and platform engineers, setting hiring bar, fostering technical growth, and driving knowledge-sharing across the function
Partner with data science and ML engineering teams to design and build CI/CD pipelines, model registries, feature stores, and observability systems that support production workloads
Drive infrastructure decisions—cloud platforms, containerization, orchestration tools—balancing cost, performance, and organizational maturity
Establish SLOs and monitoring frameworks for model performance and system health; own incident response and postmortem practices
Collaborate with security and compliance teams to embed governance, data privacy, and audit trails into ML workflows
Communicate infrastructure roadmap and technical trade-offs to leadership; align MLOps investments with business priorities
Candidate requirements
7+ years in machine learning engineering, MLOps, or data engineering, with at least 3 years in a lead or senior-level role owning platform or infrastructure
Deep hands-on experience with ML deployment pipelines, containerization (Docker, Kubernetes), and orchestration tools (Airflow, Kubeflow, or similar)
Proven track record building or scaling MLOps teams and establishing processes that improve model time-to-production and operational reliability
Fluency with cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code practices; comfort making strategic tooling decisions
Strong communication skills; ability to distill technical complexity for non-technical stakeholders and collaborate effectively across engineering and business functions
Experience with model monitoring, versioning, and governance in production environments