Senior ML Ops 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. Building and deploying predictive models is at the heart of what we do. Our Machine Learning Operations team enables our Data Scientists to build and deploy innovative models while developing cutting-edge, cloud-native capabilities to deliver predictive modeling solutions faster, more accurately, and more efficiently to 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. Optimizes resource usage to minimize infrastructure expense while maximizing performance. Monitors and maintains 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. Provides technical assistance and mentorship to all team members; troubleshoots complex issues and escalates issues as necessary. Supports the company commitment to risk management and protecting the integrity and confidentiality of systems and data. The above job description is not intended to be an all-inclusive list of duties and standards of the position. Incumbents will follow instructions and perform other related duties as assigned by their supervisor. Minimum Qualifications Education and experience typically obtained through completion of a Bachelor's degree in Computer Science, Engineering, or a related field. Minimum 5 years' experience in Data Science, ML Engineering, or ML Ops capacity. Strong programming skills in Python and experience with Data Science and ML packages and frameworks. Experience with AWS services. Proficiency with containerization technologies (Docker, Kubernetes) and CI/CD practices. Experience deploying models with MLOps tools such as MLflow, Kubeflow, or similar platforms. Expert understanding of data management, distributed computing, and software architecture principles. Proven experience delivering real-time models in production environments. Background and drug screen. Preferred Qualifications Additional related education and/or work experience preferred. Experience in hybrid (OnPrem / Cloud) environments. Hadoop / Hive / Cloudera experience. Distributed computing programming skills such as Spark. Experience with Scala / Java programming languages. Physical Requirements Early Warning works together in a highly collaborative office environment. Working conditions consist of a normal office environment. Work is primarily sedentary and requires extensive use of a computer and involves sitting for periods of approximately four hours. Work may require occasional standing, walking, kneeling, and reaching. Must be able to lift 10 pounds occasionally and/or negligible amount of force frequently. Requires visual acuity and dexterity to view, prepare, and manipulate documents and office equipment including personal computers. Requires the ability to communicate with internal and/or external customers. Employee must be able to perform essential functions

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