ML Ops Engineer

Job Summary

To support a growing data science team, the full-time ML Ops Engineer will partner with data scientists to productionize machine learning models, establish CI/CD practices, and ensure the reliability and observability of ML systems in a remote work environment.

Key responsibilities

• Productionize training and inference workflows, establishing CI/CD for models and data/versioning practices
• Centralize feature generation and manage model registry/metadata to streamline deployment workflows
• Implement monitoring for data quality, model performance, and pipeline health with alerting and dashboards

Required qualifications

• 5+ years of experience in ML Ops, managing ML infrastructure for large-scale systems
• Strong software engineering skills, particularly in Python, with extensive experience in developing automated ML pipelines
• Production experience with cloud services (AWS, DataBricks) and containerization (Docker, Kubernetes)
• Familiarity with infrastructure as code tools such as Terraform or CloudFormation
• Experience with ML tooling like MLflow or SageMaker, and knowledge of monitoring and observability practices for ML systems