MLOps Engineer

Summary

Join Experian Health as an experienced MLOps Engineer to build and scale machine learning solutions for the healthcare revenue cycle. You will operationalize ML models, ensuring deployment pipelines and maintaining scalable infrastructure on AWS. Collaborate with data scientists and engineers to bring ML products from prototype to production, emphasizing automation and continuous improvement. Develop scalable MLOps pipelines, implement infrastructure as code, and collaborate on productionizing ML models. Monitor model performance, improve ML workflows using various tools, and ensure compliance with healthcare data standards. This role offers the opportunity to work remotely with a flexible schedule.

Requirements

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field
• 3+ years' 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)
• Proficiency with containerization and orchestration tools (Docker, Kubernetes/EKS)
• 3+ years' experience with ML lifecycle tools such as MLflow, TensorFlow Serving, or Kubeflow and with CI/CD pipelines, infrastructure as code (e.g., Terraform, CloudFormation), and monitoring/logging tools
• Experience in the healthcare domain, especially with claims or EHR data, and familiarity with standards like ICD and CPT
• Familiarity with Agile development methodologies
• AWS certifications (e.g., Machine Learning Specialty, DevOps Engineer)

Responsibilities

• Develop 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
• Improve 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)

Preferred Qualifications

Exposure to NLP, Bayesian modeling, or real-time ML systems

Benefits

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