✦ Luna Orbit — AI & Machine Learning

Sr. Manager MLOps Engineering

at McKesson

📍 USA, TX, Irving Hybrid 💰 $163K – $273K USD / year Posted March 13, 2026
Salary $163K – $273K USD / year
Type Full-Time
Experience lead
Exp. Years Not specified
Education Not specified
Category AI & Machine Learning

This leadership role involves designing and executing MLOps strategies to enable scalable, secure AI solutions in healthcare, leading teams and managing cloud ML infrastructure.

  • Lead MLOps strategy
  • Mentor team
  • Manage ML infrastructure
  • Implement CI/CD pipelines
  • Ensure model governance

Focuses on cloud-native ML platforms, containerization, model lifecycle management, and governance, utilizing tools like Azure ML, Databricks, Docker, and Kubernetes.

The ideal candidate is a lead MLOps engineer with expertise in cloud-native ML platforms, containerization, and model lifecycle management, with strong leadership and collaboration skills to guide teams in deploying healthcare AI solutions.

MLOpsModel developmentCI/CDModel deploymentModel monitoringCloud-native platformsDockerKubernetes
Model retrainingGovernanceAzure Data FactoryDatabricksAWSGCPData lineageExperiment trackingReproducibility
Azure MLAKSAzure Data FactoryDatabricksAWSGCPDockerKubernetes
MLOpsModel developmentCI/CDModel deploymentModel monitoringCloud-native platformsDockerKubernetesAzure MLAKSAzure Data FactoryDatabricksAWSGCPData lineageExperiment trackingReproducibility
MLOpsMachine Learning OperationsModel developmentCI/CDModel deploymentModel monitoringModel retrainingGovernanceCloud-native platformsAzure MLAKSAzure Data FactoryDatabricksAWSGCPCloud resource managementContainerizationDockerKubernetesModel versioningExperiment trackingData lineageReproducibility
leadershipmentorshipcollaborationinnovationcommunication
Industry Healthcare & Medical
Job Function Leadership in MLOps engineering for healthcare AI
MLOpsModel developmentCI/CDModel deploymentModel monitoringModel retrainingGovernanceCloud-native platformsAzure MLAKSAzure Data FactoryDatabricksAWSGCPDockerKubernetesData lineageExperiment trackingReproducibility

Lack of experience with cloud ML platforms (Azure, AWS, GCP), No experience with Docker or Kubernetes, Less than 3 years of relevant experience, No familiarity with MLOps lifecycle

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