Principal MLOps Engineer

Role Overview

• Lead the operational architecture, deployment strategy, and reliability engineering for integrating AI into high-stakes Healthcare Information Systems (HIS)
• Define the enterprise operational standards, govern the release processes, and build the resilient infrastructure required to maintain models in mission-critical clinical environments
• Architect and govern the comprehensive release process, defining enterprise checklists, automated approval gates, release notes, and deployment readiness standards
• Establish the deployment execution standards for promoting AI across all environments and ensure customer deployments adhere to strict internal production discipline
• Architect and oversee the enterprise model registry, ensuring seamless integration with CI/CD pipelines and full version control traceability
• Define and enforce monitoring standards, establishing critical SLAs/SLOs, service health metrics, and comprehensive dashboards across the AI ecosystem
• Architect automated checks for input/output data quality and model drift, ensuring proactive detection of system degradation
• Establish and lead the production incident process, including rigorous triage workflows, severity escalation paths, postmortems, rollback mechanisms, and recovery infrastructure
• Partner with Platform teams to provide essential ATO (Authority to Operate) and compliance support, ensuring complete deployment traceability and strict operational controls
• Oversee comprehensive operational reporting, providing leadership with status updates across production systems, pre-prod testing, customer rollouts, and incident metrics
• Foster a culture of production discipline, guiding junior engineers in maintaining operational runbooks and reliable deployment pipelines

Requirements

• 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 and maintaining large-scale ML systems in production
• Expert-level experience with Cloud Providers (AWS/GCP/Azure) and orchestration tools (Kubernetes, Kubeflow, or Airflow)
• Expert-level Python and Java/Go (or similar)
• Deep proficiency in backend frameworks, microservices, and system design patterns
• Expert knowledge of monitoring stacks (Prometheus, Grafana, Datadog) and establishing enterprise SLAs/SLOs for AI services
• Proven track record of designing automated deployment pipelines, managing complex rollback procedures, and enforcing model registry governance at scale.

Tech Stack

• Airflow
• AWS
• Azure
• Cloud
• Google Cloud Platform
• Grafana
• Java
• Kubernetes
• Microservices
• Prometheus
• Python
• Go

Benefits

• Medical
• Dental & Vision
• Health Savings Accounts
• Health Care & Dependent Care Flexible Spending Accounts
• Disability Benefits
• Life Insurance
• Voluntary Benefits
• Paid Absences
• Retirement Benefits