MLOps Engineer - Healthcare (Remote)

Role Overview

MLOps Engineer (Healthcare) reporting to the Director of Analytics. You will help build and scale machine learning solutions for challenges in the healthcare revenue cycle, bringing ML products from prototype to production.

Responsibilities

• Design, build, and maintain scalable MLOps pipelines for training, validation, deployment, and monitoring.
• Implement infrastructure as code and CI/CD workflows to enable rapid experimentation and reliable production releases.
• Collaborate with data scientists to productionize ML models, ensuring reproducibility, versioning, and traceability.
• Monitor model performance and data drift; implement automated retraining and alerting.
• Optimize ML workflows using tools such as AWS SageMaker, Airflow, Docker, Kubernetes (EKS), and Step Functions.
• Ensure healthcare compliance and security best practices (e.g., HIPAA).
• Improve MLOps practices and advocate for automation and scalability across the ML lifecycle.

Requirements

• 3+ years in MLOps, DevOps, or ML engineering.
• 3+ years with AWS ML services (e.g., SageMaker, Lambda, Step Functions, S3, ECR, CloudWatch).
• 3+ years with ML lifecycle tooling such as MLflow, TensorFlow Serving, or Kubeflow.
• Proficiency with Docker and Kubernetes/EKS.
• Experience with CI/CD pipelines, infrastructure as code (e.g., Terraform, CloudFormation), and monitoring/logging tools.
• Experience working in cross-functional, collaborative teams.
• Healthcare domain experience, especially claims or EHR data; familiarity with ICD and CPT.
• Familiarity with Agile development.
• Relevant degree in Computer Science, Engineering, Data Science, or related field.

Nice to Have

• Exposure to NLP, Bayesian modeling, or real-time ML systems.
• AWS certifications (e.g., Machine Learning Specialty, DevOps Engineer).