MLOps Engineer, Machine Learning Engineer (Remote)

Experian

2d • Remote

About The Position

We are looking for an experienced MLOps Engineer to build and scale machine learning solutions that address critical challenges in the healthcare revenue cycle. You will report to Experian Health and focus on operationalizing ML models, ensuring deployment pipelines, and maintaining scalable, secure, and ML infrastructure on AWS, collaborate with data scientists, software engineers, and product teams to bring ML products from prototype to production, with a emphasis on automation, monitoring, and continuous improvement. You'll have opportunity to: 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)

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

Nice To Haves

• 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)

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)

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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees