Machine Learning Operations Engineer

Key Responsibilities

• Own and extend Circadia’s ML pipeline orchestration using Apache Airflow, including training, evaluation, and deployment workflows.
• Build and maintain automated pipelines for model retraining, validation, and promotion across development, staging, and production environments.
• Implement pipeline monitoring, alerting, and failure recovery to eliminate silent failures and ensure operational reliability.
• Design pipeline architectures that support rapid experimentation while enforcing production-grade reproducibility.
• Deploy and manage ML models on AWS infrastructure (e.g. AWS Batch for batch inference workloads).
• Support deployment of models to edge devices, including Circadia’s clinical monitoring hardware, working with firmware and embedded engineering teams as needed.
• Manage model versioning, promotion, and rollback workflows through the MLflow model registry.
• Evaluate and implement strategies for safe model rollouts (e.g. shadow deployments, canary releases) as the platform matures.
• Maintain and improve the MLflow-based experiment tracking and model registry infrastructure.
• Establish conventions for experiment logging, artifact storage, model metadata, and lineage tracking.
• Enable ML engineers to move seamlessly from experimentation to production deployment with minimal friction.
• Implement and maintain training data versioning and dataset management practices to ensure reproducibility of model training runs.
• Track dataset lineage, labeling provenance, and feature dependencies alongside model versions.
• Collaborate with ML engineers and data engineers to formalise dataset release and validation workflows.
• Build monitoring systems for model performance in production, including data drift detection, prediction quality tracking, and alerting on degradation.
• Implement operational dashboards for pipeline health, compute utilisation, and deployment status.
• Collaborate with data engineering to ensure upstream data quality and pipeline reliability for ML feature inputs.
• Develop incident response procedures and runbooks for ML system failures.
• Manage and optimise AWS compute resources (Batch, EC2, or similar) used for model training and inference.
• Design infrastructure-as-code solutions for reproducible ML environments.
• Drive cost optimisation across ML compute, storage, and data transfer.
• Support Snowflake integrations for feature generation and training data pipelines.
• Introduce and champion ML engineering best practices including CI/CD for models, automated testing for ML pipelines, and reproducible training workflows.
• Build internal tooling and templates that accelerate the ML development-to-production cycle.
• Document operational processes, architecture decisions, and onboarding materials for the ML platform.
• Participate in architecture discussions and technical planning to ensure ML systems scale with Circadia’s growth.
• Ensure all ML pipelines and infrastructure meet healthcare security and privacy requirements, including HIPAA and SOC 2.
• Apply best practices for handling Protected Health Information (PHI) in training data, model artifacts, and inference outputs.
• Maintain audit trails for model decisions, data access, and deployment history.
 

Required Qualifications

• 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.

Preferred Qualifications

• Experience deploying models to edge or embedded devices.
• Background in healthcare, medical devices, or clinical data systems.
• Familiarity with model serving frameworks (e.g., TorchServe, TF Serving, Triton, or custom solutions).
• Experience with CI/CD systems for ML (e.g., GitHub Actions, Jenkins, or similar).
• Experience with data versioning tools (e.g., DVC, LakeFS, or similar).
• Experience supporting data science or ML research teams in a production context.
• Exposure to HIPAA compliance and healthcare security best practices.
• Experience with distributed compute frameworks (e.g. Apache Spark, Dask) for large-scale data processing.
• Experience with streaming or real-time inference architectures.

What You Bring

• You take ownership of ML infrastructure end-to-end — from training pipelines to production monitoring.
• You care deeply about reliability, reproducibility, and operational excellence in ML systems.
• You have strong opinions (loosely held) on how to build a great ML platform, and you’re eager to put them into practice.
• You are comfortable working in a startup environment where you’ll wear multiple hats and move fast.
• You communicate clearly across engineering, data science, and clinical teams.
• You’re motivated by building technology that directly improves patient care.