Overview Director, Machine Learning Operations Engineering role at Early Warning. Location options include Scottsdale, San Francisco, Chicago, or New York, with a hybrid work model in these locations. Candidates must be eligible to work in the United States and this position is not eligible for employment visa sponsorship. Responsibilities Manage a team of ML Ops Engineers, overseeing day-to-day backlog and maintaining technical excellence. Develop strategic direction for the ML Ops team, platform, and infrastructure with governance, optimization, and automation in mind. Align 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. Improve existing pipelines and build next-generation tooling for model deployment. Optimize orchestration processes for efficient deployment and management of predictive models. Minimize infrastructure costs while maximizing performance through resource optimization. Monitor and maintain performance, security, and scalability of 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 as needed. Create robust monitoring systems to measure and trend model performance, detect drift, and ensure optimal production performance. Develop automation scripts and tools to improve efficiency and reliability of MLOps processes. Optimize ML workflows for efficiency, scalability, and reliability. Provide technical assistance and mentorship to team members. Support the company’s commitment to risk management and protecting the integrity and confidentiality of systems and data. The above job description is not an all-inclusive list of duties; incumbents will follow instructions and perform other related duties as assigned by their supervisor. Minimum Qualifications Bachelor's degree in Computer Science, Engineering, or related field. 12+ years of experience in Data Science, ML Engineering, or ML Ops. 5 years of experience managing highly technical employees (e.g., Data Scientists or Engineers). Expert programming skills in Python with experience in Data Science and ML packages and frameworks. Deep experience with AWS services and architecture. Experience implementing and supporting end-to-end ML workflows and patterns. Proficiency with containerization (Docker, Kubernetes) and CI/CD practices. Experience deploying models with MLOps tools (e.g., MLflow, Kubeflow) or similar platforms. Strong understanding of data management, distributed computing, and software architecture principles. Proven experience delivering real-time models in production environments. Experience in hybrid (On-Prem / Cloud) environments. Hadoop / Hive / Cloudera experience; familiarity with Scala/Java and modern distributed computing (e.g., Spark). Background and drug screening. Physical Requirements Office-based role with extensive computer use. Work is primarily sedentary; may require standing, walking, kneeling, reaching. Ability to lift up to 10 pounds occasionally. Visual acuity and dexterity to operate documents and equipment. Ability to communicate with internal and external customers. Must be able to perform essential functions with or without reasonable accommodation. Compensation and Benefits The base pay scale for this position varies by city: Phoenix, AZ and Chicago, IL: $190,000 – $250,000 per year. New York, NY and San Francisco, CA: $225,000 – $300,000 per year. Discretionary incentive plan and benefits are available. This pay scale is subject to change and not a promise of specific pay; offers depend on factors including job scope, location, education, experience, and skills. Equal employment opportunity and wage equity are emphasized by the company. We offer health benefits (medical, dental, vision), retirement plan with company match, paid time off and holidays, parental leave, and additional programs to support health and well-being. Details are available on the Benefits page and will be discussed during the interview process. Equal Opportunity Early Warning Services, LLC (“Early Warning”) hires qualified candidates without regard to race, religious creed, color, sex, sexual orientation, gender identity or expression, age, national origin, ancestry, citizenship, protected veteran or disability status, or any other factor prohibited by law. The company maintains an equal employment opportunity and affirmative action policy in accordance with applicable laws.

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