Senior MLOps Engineer

Job Summary

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