Director, MLOps

Axial Search; United States; In loco; USD 210.000 - 330.000; Tempo pieno

Descrizione del lavoro

Axial Search is seeking an experienced MLOps leader to drive strategy, governance, and the deployment lifecycle for AI initiatives across the organization. You will shepherd end-to-end ML deployment, monitoring, and platform reliability while mentoring a growing team of engineers.

You will partner with data science and ML teams to build robust CI/CD, model registries, and observability, balancing cost, performance, and risk as products scale across multiple domains.

Competenze

• 7+ years in ML engineering, MLOps, or data engineering with lead experience.
• Hands-on deployment pipelines, containers, and orchestration.
• Experience with cross-functional teams and governance.
• Fluency with cloud platforms (AWS, GCP or Azure).
• Strong communication to translate technical constraints for business outcomes.

Mansioni

• 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.

Conoscenze

Leadership

MLOps

Cloud platforms

CI/CD for ML

Data engineering basics

Communication

Strumenti

Docker

Kubernetes

Airflow

Kubeflow

Descrizione del lavoro

Axial Search is a specialist executive search firm built for one kind of hire: leaders who help organizations navigate AI transformation.

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 say 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