Director of Software Engineering; MLOps & ML Governance

Our vision for the future is based on the idea that transforming financial lives starts by giving our people the freedom to transform their own. We have a flexible work environment, and fluid career paths. We not only encourage but celebrate internal mobility. We also recognize the importance of purpose, well-being, and work-life balance. Within Empower and our communities, we work hard to create a welcoming and inclusive environment, and our associates dedicate thousands of hours to volunteering for causes that matter most to them.

Chart your own path and grow your career while helping more customers achieve financial freedom. Empower Yourself.

Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment visa at this time, including CPT/OPT.

• Leads the execution of deliverables with the risk and engineering teams ensuring projects are completed on time and within predefined requirements and budgets.
• Accountable for team performance within assigned projects, including software engineering and MLOps initiatives.
• Serves as subject matter expert on multiplatform applications and ML infrastructure, determining course of action under the direction of systems leadership.
• Directs the system development lifecycle including the design, coding, testing, documentation, maintenance, and support of proprietary and purchased software systems and applications.
• Leads the design, implementation, and scaling of enterprise MLOps platforms and capabilities, including CI/CD pipelines for ML, model versioning, automated testing, monitoring, and model lifecycle management.
• Establishes best practices for model deployment, observability, performance monitoring, drift detection, and retraining strategies.
• Partners with Data Science, Risk, Compliance, Legal, and Information Security teams to ensure machine learning systems meet regulatory, governance, and risk management requirements.
• Implements and oversees ML governance frameworks including model documentation, validation, explainability, auditability, fairness/bias monitoring, and model risk management controls.
• Ensures adherence to enterprise data governance, privacy, and AI risk policies, including responsible AI standards.
• Recommends innovation and improvements to policy or procedures; has the latitude to make decisions affecting mid- to long-term operational results.
• Develops long- and short-term business and technology strategies aligned to AI/ML roadmaps.
• Responsible for managing operating costs and budgets, including infrastructure investments for ML platforms. Makes decisions on pay, performance, appraisals, schedules, discipline, and hiring.
• Builds and leads a high-performing MLOps and engineering organization, establishing clear career paths and leadership development for managers and senior engineers.

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field; or equivalent combination of professional experience and/or training required.
• 10+ years of experience in software engineering, including experience building distributed or cloud-based systems.
• 4+ years successfully managing teams (including managers), with demonstrated ability to develop, motivate, and direct leaders and technical contributors.
• Proven experience leading or building an MLOps function or production-grade ML platforms in an enterprise environment.
• Strong understanding of ML lifecycle management including model training, validation, deployment, monitoring, and governance.
• Experience implementing ML governance and model risk management frameworks (e.g., documentation standards, validation controls, bias/fairness testing, explainability techniques, audit readiness).
• Familiarity with regulatory expectations for AI/ML in highly regulated industries (e.g., financial services, healthcare, etc., as applicable).
• Experience with cloud platforms (AWS, Azure, GCP), containerization (Docker/Kubernetes), CI/CD pipelines, and infrastructure-as-code.
• Strong understanding of data governance, privacy, security, and compliance considerations related to AI/ML systems.
• Ability to manage a leadership pipeline by mentoring subordinate managers and nurturing senior technical talent.
• Excellent executive communication skills with the ability to translate complex ML and risk topics into business-aligned decisions.

This job description is not intended to be an exhaustive list of all duties, responsibilities and qualifications of the job. The employer has the right to revise this job description at any time. You will be evaluated in part based on your performance of the responsibilities and/or tasks listed in this job description. You may be required perform other duties that are not included on this job description.

The job description is not a contract for employment, and either you or the employer may terminate employment at any time, for any reason.

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