Senior MLOps Engineer, ClimateAi

• 3–5 years of experience in Machine Learning, Backend Software Engineering, Data Engineering, or MLOps roles supporting data-intensive systems in production
• Production experience with ML lifecycle management platforms such as MLFlow, Weights & Biases, Neptune.ai, Comet.ml or similar
• Experience with IaC using Terraform, Pulumi, OpenTofu, Encore, Crossplane or similar
• Deep experience with building systems-of-systems in AWS, GCP, or Azure, that span across multiple services or multiple cloud providers
• Strong communication and ownership. You scope your work, monitor what you ship, and drive problems to permanent resolution
• Ability to collaborate closely with Data Scientists, Engineers, and Product, to design and support end-to-end ML workflows

Desirable

• As a Senior MLOps Engineer, you will own the infrastructure and ML platform that powers ClimateAi’s forecasting and risk products
• You will design, build, and operate the cloud systems, data management infrastructure, and model lifecycle tooling that allow our Data Science and ML Engineering teams to develop, compare, register, and ship models with confidence
• This is a high-leverage role at the intersection of infrastructure, ML platform, and security
• You will partner closely with Data Science to unblock initiatives like SYO2 and Risk Outlooks model improvements by giving them a real model management platform; with Data Engineering to harden our data lakehouse and pipelines; and with our security lead to provide a strong second engineer on cloud security — building skill duplication across critical systems
• Stand up and operate the ML model framework that provides ML engineers and data scientists experiment tracking, model registry, and lineage
• Own and evolve our Infrastructure as Code so environments are reproducible, auditable, and easy for engineers to extend
• Build CI/CD and deployment patterns for ML pipelines and models, including reproducible training, automated validation, and safe rollouts to production
• Improve data management systems alongside Data Engineering with storage tiering, lifecycle and retention, cost-performance tradeoffs, and observability across our cloud environments
• Author clear architectural documentation, runbooks, and design proposals to communicate tradeoffs to engineering and non-technical stakeholders