ML Ops Engineer

Role:

• ML platform: Productionize training and inference, establish CI/CD for models, data/versioning practices, and model governance.
• Feature & model lifecycle: Centralize feature generation, manage model registry/metadata, streamline deployment workflows.
• Observability & quality: Implement monitoring for data quality, drift, model performance, and pipeline health with clear alerting and dashboards.

Requirements:

• Experience: 5+ years in ML Ops, including ownership of ML infrastructure for large-scale systems.
• Software engineering strength: Strong coding, debugging, performance analysis, testing, and CI/CD discipline.
• Cloud & containers: Production experience on AWS, DataBricks, Docker + Kubernetes (EKS/ECS or equivalent).

Benefits:

• Competitive compensation, flexible remote work.
• Unlimited Responsible PTO.
• Great opportunity to join a growing, cash-flow-positive company.