Senior MLOps Engineer

Bridging the gap between machine learning model development and core system operations, the full-time remote Senior MLOps Engineer II will design, build, and scale infrastructure and automated pipelines for training, deploying, and monitoring machine learning models in production environments. Key responsibilities Design, implement, and manage automated CI/CD and Continuous Training pipelines for machine learning model development Containerize, deploy, and scale machine learning models as high-availability microservices or batch processing workflows Establish unified logging, alerting, and monitoring solutions to track model performance and system metrics Required qualifications 5+ years of professional experience in software engineering, DevOps, or data engineering, with at least 2 years focused on MLOps infrastructure Strong proficiency in Python and familiarity with software engineering best practices Hands-on experience with containerization (Docker) and orchestration platforms (Kubernetes) Proven familiarity with ML lifecycle and data processing tools such as MLflow, Kubeflow, and SparkML Practical experience in a major cloud ecosystem (AWS, GCP) with knowledge of cloud networking and security