Machine Learning Ops Engineer

Job Summary

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