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

3+ years of experience in MLOps, DevOps, or ML engineering roles
3+ years experience with AWS services for ML (e.g., SageMaker, Lambda, Step Functions, S3, ECR, CloudWatch)
3+ years Experience with ML lifecycle tools such as MLflow, TensorFlow Serving, or Kubeflow
Proficiency with containerization and orchestration tools (Docker, Kubernetes/EKS)
Experience with CI/CD pipelines, infrastructure as code (e.g., Terraform, CloudFormation), and monitoring/logging tools
Experience working in collaborative, cross-functional teams
Experience in the healthcare domain, especially with claims or EHR data, and familiarity with standards like ICD and CPT
Exposure to NLP, Bayesian modeling, or real-time ML systems
Familiarity with Agile development methodologies
AWS certifications (e.g., Machine Learning Specialty, DevOps Engineer)
Bachelor's degree in Computer Science, Engineering, Data Science, or a related field

Great compensation package and bonus plan
Core benefits including medical, dental, vision, and matching 401K
Flexible work environment, ability to work remote, hybrid or in-office
Flexible time off including volunteer time off, vacation, sick and 12-paid holidays

MLOps Engineer – Healthcare

Key skills