ClimateAi
Climate Tech
SeniorMLOpsEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior MLOps Engineer at ClimateAi. Skills: MLOps, Infrastructure, ML platform, Security. Stand up ML model framework. Operate ML model framework”
Industry & Context.
Drive problems to resolution
What They're Looking For.
Must Have
3-5 years experience, Production experience ML lifecycle, Experience with IaC, Deep experience building systems
Nice to Have
Experience training ML models, Experience configuring models datasets
What You'll Do.
Stand up ML model framework
Operate ML model framework
Own Infrastructure as Code
Evolve Infrastructure as Code
Build deployment patterns
Improve data management systems
Author architectural documentation
How You'll Work.
Team & Collaboration
Partner with Data Science; Partner with Data Engineering; Partner with security lead; Cross-functional teams; Data Scientists; Engineers; Product
Communication Scope
Architectural documentation; Design proposals
Full Job Description
At ClimateAi, we choose to act. We believe resilience is just as urgent as mitigation. We are building technology to empower people and industries to make smarter, faster decisions in the face of weather volatility. Our mission is to climate-proof the global economy, with the goal of achieving zero loss of lives, livelihoods, and nature. From farmers and supply chain managers to risk analysts and policymakers, our users depend on ClimateAi’s forecasts and insights to prepare for what’s coming and take action in time. In 2022, ClimateAi was recognized by TIME Magazine’s Best Inventions, alongside innovators like OpenAI, for our breakthrough work in climate resilience technology. What if your next position helped protect entire communities and safeguard the future of food, water, and livelihoods? The Role 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. Our hybrid work schedule includes 3 in-person days at one of our core locations (SF, Boston) and 2 remote days. What You’ll Do 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 f
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