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
Experience: 2 years required