Senior ML Operations Engineer

At Early Warning, we’ve powered and protected the U.S. financial system for over thirty years with cutting-edge solutions like Zelle®, Paze℠, and so much more. As a trusted name in payments, we partner with thousands of institutions to increase access to financial services and protect transactions for hundreds of millions of consumers and small businesses. Positions located in Scottsdale, San Francisco, Chicago, or New York follow a hybrid work model to allow for a more collaborative working environment. Candidates responding to this posting must independently possess the eligibility to work in the United States, for any employer, at the date of hire. This position is ineligible for employment Visa sponsorship. At Early Warning, we’ve powered and protected the U.S. financial system for over thirty years with cutting-edge solutions like Zelle®, Paze℠, and so much more. As a trusted name in payments, we partner with thousands of institutions to increase access to financial services and protect transactions for hundreds of millions of consumers and small businesses. Building and deploying predictive models is at the heart of what we do. Our Machine Learning Operations team enables our Data Scientists to be able to build and deploy innovative models while developing cutting edge, cloud native capabilities to deliver predictive modeling solutions faster, more accurate, and more [... source excerpt omitted ...] help keep fraud and bad actors out of the banking system. Overall Purpose This position is responsible for the platforms, tools, and processes that take our models from ideas to production models, serving predictions in real time. The Sr. ML Ops Engineer will partner with our Data Science, Data Product Management, Product Engineering, and Data Platform teams to create and support tools and processes to automate model productionalization. Essential Functions: Designs, builds, and maintains scalable ML infrastructure and pipelines for model training, deployment, and monitoring. Optimizes orchestration processes to ensure efficient deployment and management of predictive models. [... source excerpt omitted ...] s the performance, security, and scalability of the ML infrastructure. Collaborates with data scientists and software engineers to streamline the ML lifecycle from development to production. Develops and maintains tools for data analysis, experimentation, model versioning, and artifact management. Supports data and model governance requirements as needed. Creates robust monitoring systems to measure and trend model performance, detect model drift, and ensure optimal performance of models in production. Develops automation scripts and tools to improve the efficiency and reliability of MLOps processes. Optimizes ML workflows for efficiency, scalability, and reliability. Provide