Machine Learning Ops Engineer

To support the Product & Technology Organization, the full-time Machine Learning Ops Engineer will design and maintain infrastructure for scaling ML models, focusing on automated pipelines, model serving, and compliance with HIPAA regulations. Key responsibilities Design and implement automated ML pipelines for model training, evaluation, and deployment using MLflow and AWS SageMaker Build and manage scalable model serving infrastructure using Docker and Kubernetes for real-time and batch scoring Establish CI/CD workflows and production monitoring for data drift, model decay, and pipeline failures Required qualifications Bachelor's or Master's Degree in Computer Science, Software Engineering, or a related technical field 7+ years of professional experience in DevOps, Data Engineering, or ML Engineering, with at least 4 years focused on Machine Learning operations Proven experience in operating production ML systems, including model serving and lifecycle governance Expert-level skills in containerization and orchestration with Docker and Kubernetes/EKS Strong proficiency in AWS services relevant to machine learning and data processing