Principal MLOps Engineer

Details

• Release Strategy:Architect and govern the comprehensive release process, defining enterprise checklists, automated approval gates, release notes, and deployment readiness standards.
• Deployment Execution:Establishthe deployment execution standards for promoting AI across all environments and ensure customer deployments adhere to strict internal production discipline.
• Model Registry:Architect and oversee the enterprise model registry, ensuring seamless integration with CI/CD pipelines and full version control traceability.
• Monitoring Standards:Define and enforcemonitoringstandards,establishingcritical SLAs/SLOs, service health metrics, and comprehensive dashboards across the AI ecosystem.
• Quality & Drift Control:Architect automated checks for input/output data quality and model drift, ensuring proactive detection of system degradation.
• Incident Framework:Establishand lead the production incident process, including rigorous triage workflows, severity escalation paths, postmortems, rollback mechanisms, and recovery infrastructure.
• Security & Traceability:Partner with Platform teams to provide essential ATO (Authority to Operate) and compliance support, ensuring complete deployment traceability and strict operational controls.
• Operational Visibility:Oversee comprehensive operational reporting, providing leadership with status updates across production systems, pre-prod testing, customerrollouts, and incident metrics.
• Operational Excellence:Foster a culture of production discipline, guiding junior engineers inmaintainingoperational runbooks and reliable deployment pipelines.
• Bachelor's Degree or Higher in Computer Science, Software Engineering, or related technical field.
• 10+ years of experience in software engineering, with at least 6 years dedicated to deploying andmaintaininglarge-scale ML systems in production (not just research or POCs).
• MLOps& Cloud:Expert-level experience with Cloud Providers (AWS/GCP/Azure) and orchestration tools (Kubernetes, Kubeflow, or Airflow).
• Engineering & Programming:Expert-level Python and Java/Go (or similar). Deepproficiencyin backend frameworks, microservices, and system design patterns.
• Observability & Telemetry:Expert knowledge of monitoring stacks (Prometheus, Grafana, Datadog) andestablishingenterprise SLAs/SLOs for AI services.
• Release Engineering:Proventrack recordof designing automated deployment pipelines, managing complex rollback procedures, and enforcing model registry governance at scale.
• Master’s or PhD in Computer Science, Software Engineering, orrelatedtechnical field is preferred.
• Security & Compliance:Deep understanding of cybersecurity best practices and ATO processes within regulated industries (Healthcare, Finance, or Defense).
• Distributed Systems:Proven ability to design systems that handle massive concurrency and distributed data processing.
• Remote