Sr. Manager MLOps Engineering

Sr. Manager, MLOps Engineering

Lead the design and execution of McKesson's MLOps strategy to enable scalable, secure, and efficient machine learning operations. This role drives innovation, governance, and collaboration across data science and engineering teams to deliver impactful AI solutions in healthcare.

• Lead, mentor, and develop a high-performing MLOps engineering team, fostering a collaborative, innovative, and accountable culture aligned with organizational goals.
• Define and drive the end-to-end MLOps lifecycle, including model development, CI/CD, testing, deployment, monitoring, retraining, and governance.
• Architect and maintain scalable, secure, and cost-efficient ML infrastructure using cloud-native platforms (e.g., Azure ML, AKS, ADF, Databricks; AWS/GCP as needed).
• Build and optimize automated pipelines and tools for model training, deployment, observability, and incident response to improve operational reliability and reduce manual effort.
• Partner with data scientists, software engineers, product teams, and platform groups to deliver effective, repeatable, and well-governed MLOps workflows.
• Establish and enforce best practices for model versioning, experiment tracking, data lineage, reproducibility, and model lifecycle documentation.
• Implement and maintain robust monitoring, logging, and alerting systems to track model performance, data drift, system health, and infrastructure metrics.
• Ensure compliance with Responsible AI standards, security protocols, and regulatory requirements, collaborating with legal and risk teams.
• Oversee cloud resource allocation, budgeting, and cost optimization across ML workloads in partnership with finance and procurement.
• Maintain comprehensive documentation, SOPs, and governance frameworks to support audits and ensure consistent and transparent MLOps practices.
• Stay current with evolving MLOps trends, tools, and best practices, evaluating and integrating relevant innovations to enhance the platform.

• Proven experience leading MLOps or ML engineering teams in enterprise environments.
• Expertise in cloud-native ML platforms (Azure ML, Databricks, AKS; AWS/GCP optional).
• Strong knowledge of CI/CD for ML, containerization (Docker), orchestration (Kubernetes), and ML observability tools.
• Solid understanding of model governance, Responsible AI principles, and compliance frameworks.
• Excellent leadership, stakeholder management, and communication skills.

• Degree or equivalentexperience. Typically requires9+ years of professional
  experience and 1+ years ofsupervisory and/or managementexperience.

• Experience with healthcare data compliance (HIPAA, PHI).
• Familiarity with ML monitoring tools (e.g., MLflow, EvidentlyAI).
• Knowledge of cost optimization strategies for cloud ML workloads.
• Advanced degree in Computer Science, Data Engineering, or related field.