Senior MLOps Engineer

<div class="content-intro"><p><strong>At ClimateAi, we choose to act.</strong></p> <p>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.</p> <p>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.</p> <p>In 2022, ClimateAi was recognized by <strong>TIME Magazine’s Best Inventions</strong>, alongside innovators like OpenAI, for our breakthrough work in climate resilience technology.</p> <p><strong>What if your next position helped protect entire communities and safeguard the future of food, water, and livelihoods?</strong></p></div><h3><strong>The Role</strong></h3> <p>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.</p> <p>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.</p> <p>Our hybrid work schedule includes 3 in-person days at one of our core locations (SF,  Boston) and 2 remote days.</p> <h3><strong>What You’ll Do</strong></h3> <ul> <li>Stand up and operate the ML model framework that provides ML engineers and data scientists experiment tracking, model registry, and lineage</li> <li>Own and evolve our Infrastructure as Code so environments are reproducible, auditable, and easy for engineers to extend</li> <li>Build CI/CD and deployment patterns for ML pipelines and models, including reproducible training, automated validation, and safe rollouts to production</li> <li>Improve data management systems alongside Data Engineering with storage tiering, lifecycle and retention, cost-performance tradeoffs, and observability across our cloud environments</li> <li>Author clear architectural documentation, runbooks, and design proposals to communicate tradeoffs to engineering and non-techn