Director, Machine Learning Operations Engineering

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.

Overall Purpose

This position is responsible leading the team that owns and operates the platforms, tools, and processes that take our models from ideas to production models, serving predictions in real time, and monitoring deployments to ensure quality predictions and stable platforms. The Director, ML Ops will be responsible for leading ML Ops Engineers and coordinating actions, roadmaps, and backlogs with Product Management and senior leadership.  

Essential Functions:

• Manage team of ML Ops Engineers. Manage day-to-day backlog of activities. Maintain technical excellence.

• Develop strategic direction for ML Ops team, platform, and infrastructure with an eye towards governance, optimization, and automation. Coordinate ML Ops strategy with Enterprise analytics technology and Analytics Data Platform roadmaps.

• Design, build, and maintain scalable ML infrastructure and pipelines for model training, deployment, and monitoring. Identify and implement improvements to existing modeling pipelines, while building next generation tooling to support model deployment

• Optimize orchestration processes to ensure efficient deployment and management of predictive models

• Optimize resource usage to minimize infrastructure expense while maximizing performance

• Monitor and maintain the performance, security, and scalability of the ML infrastructure

• Collaborate with data scientists and software engineers to streamline the ML lifecycle from development to production

• Develop and maintain tools for data analysis, experimentation, model versioning, and artifact management. Support data and model governance requirements as needed.

• Create robust monitoring systems to measure and trend model performance, detect model drift, and ensure optimal performance of models in production

• Develop automation scripts and tools to improve the efficiency and reliability of MLOps processes

• Optimize ML workflows for efficiency, scalability, and reliability

• Provide technical assistance and mentorship to all team members

• 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

• Bachelor's degree in Computer Science, Engineering, or a related field

• 12+ years experience in Data Science, ML Engineering or ML Ops capacity

• 5 years experience managing highly technical employees such as Data Scientists or Engineers.

• Expert level programming skills in Python and experience with Data Science and ML packages and frameworks

• Deep experience with AWS services and architecture

• Experience implementing and supporting end-to-end Machine Learning workflows and patterns

• 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

• Experience in hybrid (OnPrem / Cloud) environments

• Hadoop / Hive / Cloudera experience

• Experience with Scala / Java programming languages and modern distributed computing technologies such as Spark

• Background and drug screen.

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 ex