Director of Software Engineering – MLOps, ML Governance

Role Overview

• 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.

Requirements

• 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.

Tech Stack

• AWS
• Azure
• Cloud
• Docker
• Google Cloud Platform
• Kubernetes

Benefits

• Medical, dental, vision and life insurance
• Retirement savings – 401(k) plan with generous company matching contributions (up to 6%), financial advisory services, potential company discretionary contribution, and a broad investment lineup
• Tuition reimbursement up to $5,250/year
• Business-casual environment that includes the option to wear jeans
• Generous paid time off upon hire – including a paid time off program plus ten paid company holidays and three floating holidays each calendar year
• Paid volunteer time — 16 hours per calendar year
• Leave of absence programs – including paid parental leave, paid short
• and long-term disability, and Family and Medical Leave (FMLA)
• Business Resource Groups (BRGs) – BRGs facilitate inclusion and collaboration across our business internally and throughout the communities where we live, work and play. BRGs are open to all.