Senior MLOps Engineer
Core
Design, build, and operate the cloud systems, data management infrastructure, and model lifecycle tooling that allow Data Science and ML Engineering teams to develop, compare, register, and ship models with confidence.
Role type
Senior MLOps Engineer
Builds
Cloud systems, data management infrastructure, and model lifecycle tooling for forecasting and risk products
Domain
Climate resilience technology / Machine Learning Operations
Deliverable
infrastructure
Required skills
ML lifecycle management platforms, Infrastructure as Code, CI/CD for ML pipelines, multi-cloud systems architecture, architectural documentation
Preferred skills
Training, inference, deploying, and scaling modern ML models, petabyte-scale dataset configuration
Technologies
MLFlow, Weights & Biases, Neptune.ai, Comet.ml, Terraform, Pulumi, OpenTofu, Encore, Crossplane, AWS, GCP, Azure
Responsibilities
Operate the ML model framework providing experiment tracking, model registry, and lineage; Own and evolve Infrastructure as Code for reproducible environments; Build CI/CD and deployment patterns for ML pipelines; Improve data management systems with storage tiering and observability; Author architectural documentation and runbooks
Seniority
Senior, self-directed engineer with technical mentorship
