Staff MLOps Engineer

Description du poste

Our satellite and AI-powered products are changing how utilities see and protect the grid, and we’re gearing up for aggressive customer growth over the next two years That growth runs on AI at scale We’re building a brand-new Data Inference AI Pipeline team to deliver it, and we’re looking for a Staff MLOps Engineer to architect its operational backbone from the very first line of infrastructure code As the very first engineer on this team, you’ll start with a blank canvas and a rare chance to build something that lasts You’ll shape how our models are deployed, how the pipeline scales and recovers under load, and what it costs to run Just as importantly, you’ll help define the culture, engineering standards, and ways of working that every future teammate inherits This is a hands-on, high-leverage individual contributor role: your decisions will set the foundation the entire team builds on, and you’ll be the go-to technical authority on MLOps as the team grows You’ll report to the Director of Engineering for the Data Inference AI Pipeline team Our pending acquisition by Schneider Electric will accelerate our mission as we bring our combined offering to our joint customer base, and the infrastructure you build will power outcomes at an even greater scale Deployment Own the model deployment pipeline end-to-end: packaging, versioning, rollout, and rollback for ML models moving from training to production Build and maintain deployment infrastructure on SageMaker (endpoints, batch transform, multi-model/multi-container hosting), and evaluate where alternatives such as self-hosted serving or other managed options are a better fit Set the CI/CD pattern for model releases, including automated testing, staged rollout, and canary/shadow deployments Scalability & elasticity Design inference infrastructure that scales seamlessly with real-time and batch demand Right-size compute (CPU/GPU/Inferentia or equivalent) for each model and workload to balance latency and

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