✦ Luna Orbit — Data & Analytics

Senior MLOps Engineer

at Leidos Holdings

📍 2 Locations Unknown 💰 $107K – $195K USD / year Posted March 13, 2026
Salary $107K – $195K USD / year
Type Full-Time
Experience senior
Exp. Years Not specified
Education Not specified
Category Data & Analytics

This role involves designing and maintaining scalable AI/ML systems for the Department of War, focusing on model deployment, security, and system reliability.

  • Build ML pipelines
  • Implement model lifecycle workflows
  • Collaborate on system architecture
  • Ensure cybersecurity compliance
  • Support enterprise AI capabilities

The technical scope includes ML pipelines, system engineering, cybersecurity, cloud infrastructure, APIs, and site reliability engineering, supporting enterprise data analytics.

The ideal candidate is a senior MLOps engineer with experience in deploying and maintaining machine learning pipelines, systems engineering, and cybersecurity within enterprise or defense environments. They are mission-driven and skilled in cloud infrastructure and AI/ML deployment.

Experience with machine learning pipelinesSystems engineering experienceCybersecurity knowledgeCloud infrastructure experience
Experience with AI/ML deploymentSecurity standards knowledgeNational security experienceSupport for enterprise data and analytics products
Cloud platformsAPIsCybersecurity toolsData storage systemsLogging and auditing tools
Machine LearningML pipelinesModel deploymentModel validationModel monitoringLifecycle managementSystems engineeringCybersecurityAPICloudSREData storageLoggingAuditing
Machine LearningML pipelinesModel deploymentModel validationModel monitoringLifecycle managementModel versioningDrift detectionContinuous retrainingSystems engineeringCybersecurityAPI developmentCloud environmentNetworkData storageLoggingAuditingSystem architectureSRESite Reliability Engineering
collaborationproblem-solvingcommunicationadaptabilitymission-driven
Industry Defense
Job Function Senior MLOps Engineer supporting AI/ML deployment and system reliability
Machine LearningML pipelinesModel deploymentModel validationModel monitoringLifecycle managementSystems engineeringCybersecurityAPICloud environmentSite Reliability EngineeringSREData storageLoggingAuditingCloud

Lack of experience with ML pipelines, No systems engineering background, No cybersecurity knowledge

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