Lead Director, AI Governance Engineering

CVS Health•Work At Home-Virginia, VA

6d•$144,200 - $288,400

About The Position

AI adoption across the organization is accelerating, and existing governance processes can add cycle time and rely heavily on manual review. We are building an AI Governance Engineering capability to modernize governance through automation—shifting from approval-heavy workflows to registration-driven workflows with built-in controls, and continuous observability. As Lead Director, AI Governance Engineering, you will create and lead a lean engineering team responsible for delivering governance-as-code: platform-agnostic SDKs, APIs, and automated controls that help teams move faster while ensuring responsible AI guardrails are consistently enforced.

Requirements

Experience leading software or platform engineering teams.
Strong system architecture skills across APIs, automation, CI/CD, and data platforms.
Experience building developer-facing tools such as SDKs, templates, or automation frameworks.
Familiarity with ML and Generative AI lifecycles and how governance controls can be validated through automation.
Ability to translate governance or risk requirements into scalable technical solutions.

Nice To Haves

Experience with Responsible AI concepts such as fairness, transparency, explainability, and auditability.
Experience integrating automated quality gates into Git-based workflows and CI/CD pipelines.
Background working in regulated or high-trust technology environments.

Responsibilities

Automated Use Case Intake & Risk Classification: Design unified intake APIs and user experiences that reduce friction and automatically populate metadata and integrate with application registration and provisioning workflows. Implement dynamic intake flows for ML, Generative AI, and agentic use cases. Deliver automated inherent risk scoring and risk-based routing to provisioning and monitoring workflows.
Governance-as-Code Toolkits: Partner with AI Governance Data Scient to develop a platform-agnostic Python/SDK offering automated governance controls such as bias, drift, and AI safety testing. Integrate governance checks into CI/CD pipelines so evidence is generated during development.
Enterprise Model & Use Case Registry Integration: Enable event-driven ingestion of intake data and model artifacts into the enterprise registry. Support automated generation of audit-ready compliance artifacts and APIs for reviewers and auditors.
Self-Service Enablement & Automation: Expand policy-based routing for low- and high-risk AI use cases. Automate proof-of-concept lifecycle patterns and accelerate access to approved AI environments.
Governance for Agentic Automation: Establish execution safety patterns for internal AI agents, including permission-scoped actions. Partner with information security to implement runtime guardrails such as prompt filtering, data protection controls, and real-time observability.
Leadership & Collaboration: Build and lead a small, high-impact AI Governance Engineering team. Partner with AI platform teams, governance, security, and privacy partners to embed technical controls into development workflows. Define and track outcomes such as reduced intake complexity, faster cycle times, and improved governance transparency.