ML Ops Engineer

As an ML Ops Engineer at Circadia Health, you will own the infrastructure and operational lifecycle of the machine learning systems that power our clinical monitoring platform. You will build and maintain the production ML pipelines, deployment infrastructure, and monitoring systems that enable Circadia's predictive models to identify early signs of clinical deterioration.

Requirements

• 4+ years of experience in MLOps, ML Engineering, DevOps, or a closely related infrastructure role
• Strong proficiency in Python for ML pipeline development, tooling, and automation
• Hands-on experience with ML pipeline orchestration tools, particularly Apache Airflow
• Experience with model registries and experiment tracking platforms (MLflow preferred)
• Experience deploying and operating ML workloads on AWS (Batch, EC2, S3, IAM, CloudWatch)
• Solid understanding of the ML lifecycle: training, evaluation, deployment, monitoring, and retraining
• Experience with containerisation (Docker) and infrastructure-as-code
• Proficiency with Git and version control workflows
• Familiarity with SQL and data warehousing platforms (Snowflake preferred)
• Experience implementing monitoring, logging, and alerting for production systems
• Strong debugging and incident response skills for complex distributed systems

Benefits

• Opportunity to work on real-world healthcare problems with measurable patient impact
• Chance to build data systems that power clinical-grade AI and ML
• Ownership in a fast-growing, mission-driven company
• Collaboration with a highly skilled, multidisciplinary team