Senior ML Ops Engineer

Why this role exists

Our models decide whether a care team walks into a room tonight. They train on 70,000 years of continuous vital signs joined to clinical records from more than 400,000 unique patients. When one of them degrades it does not surface as an error rate. It surfaces as a patient who deteriorated and nobody was alerted, which means the platform that trains, ships, and watches those models carries real clinical weight. You will own it: the pipelines, the path to production, and the monitoring that catches degradation before a clinician would.

What you'll own

• Pipeline orchestration. Training, evaluation, and deployment workflows in Airflow, with automated retraining, promotion, and failure recovery.

• Deployment and release. Models onto our platform on AWS including Batch, with versioning and rollback through MLflow, maturing toward shadow and canary releases.

• Tracking and lineage. MLflow registry, conventions for artifacts and metadata, and dataset versioning so training runs are reproducible.

• Monitoring and drift. Drift, prediction quality, and degradation alerting on models where degradation is clinically consequential.

• ML compute and cost. AWS compute for training and inference, infrastructure-as-code, and cost optimization.

• Hands-on model work. Contributing to model development alongside the ML engineering team, as a secondary focus behind the platform.

• Compliance. HIPAA and SOC 2 across pipelines, with sound PHI handling in training data, artifacts, and outputs.

Required Qualifications

• 4+ years in MLOps, ML engineering, DevOps, or a closely related infrastructure role
• Strong Python for pipeline development, tooling, and automation
• Hands-on Airflow, and a model registry such as MLflow
• Deploying and operating ML workloads on AWS (Batch, EC2, S3, IAM, CloudWatch)
• Containerization, infrastructure-as-code, SQL, and Snowflake
• Building monitoring and alerting for production systems
• Enough model development experience to contribute alongside ML engineers

Preferred

• Model serving frameworks or data versioning tools
• Healthcare, medical devices, or clinical data systems
• Significant open source, systems that outlived your tenure, or a high-bar engineering background