Lead Director - AI Governance Engineering
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What you'll do
- 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.
- 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.
- 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.
- 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.
- 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.
- Build and lead a small, high-impact AI Governance Engineering team.
What they're looking for
- 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 have
- 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.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time.
Position Summary
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.
Key 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.
Required Qualifications
- 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.
Preferred Qualifications
- 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.
Education
Bachelor's degree or equivalent work experience in Mathematics, Statistics, Computer Science, Analytics, Engineering, or related discipline. Master's degree preferred
What Success Looks Like
- AI intake processes are simpler, faster, and largely automated.
- Engineering teams have reusable SDKs and templates that make responsible AI the default.
- Validation of controls shifts from self-reporting to automated checks and evidence.
- Centralized observability provides real-time insight into AI usage, performance, and risk.
If you are excited about building trusted AI systems through practical engineering, we encourage you to apply—even if you do not meet every qualification listed. We value collaboration, learning, and curiosity.
Pay Range
The typical pay range for this role is:
$144,200.00 - $288,400.00
This pay range represents the base hourly rate or base annual full-time salary for all positions in the job grade within which this position falls. The actual base salary offer will depend on a variety of factors including experience, education, geography and other relevant factors. This position is eligible for a CVS Health bonus, commission or short-term incentive program in addition to the base pay range listed above. This position also includes an award target in the company’s equity award program.
Our people fuel our future. Our teams reflect the customers, patients, members and communities we serve and we are committed to fostering a workplace where every colleague feels valued and that they belong.
Great benefits for great people
We take pride in offering a comprehensive and competitive mix of pay and benefits that reflects our commitment to our colleagues and their families.
Additional details about available benefits are provided during the application process and on Benefits Moments.
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.
Company
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