Key skills
MLOpsDevOpsMachine Learning EngineeringAWS services for MLSageMakerLambdaStep FunctionsS3ECRCloudWatchML lifecycle toolsMLflowTensorFlow ServingKubeflowContainerizationDockerKubernetesEKSCI/CD pipelinesInfrastructure as CodeTerraformCloudFormationMonitoring toolsLogging toolsHealthcare domain experienceHealthcare claims dataEHR dataICD standardsCPT standardsNLPBayesian modelingReal-time ML systemsAgile development methodologiesAWS certificationsMachine LearningMLTensorFlowAirflowAWSAgileCI/CD
About this role
Experian is a global data and technology company, empowering opportunities for people and businesses worldwide. They are seeking an MLOps Engineer to build and scale machine learning solutions for healthcare revenue cycles, collaborating with data scientists and software engineers to bring ML products from prototype to production.
Responsibilities:
- 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
Requirements:
- 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