Senior Principal AI Governance Engineer

What you'll do

• Translate AI policies and requirements into scalable technical controls and automated governance workflows.
• Design and implement technical capabilities to support the AI Governance Framework across the entire lifecycle.
• Develop tools to identify, register, classify, and assess AI use cases using a common enterprise taxonomy.
• Create reusable governance components that can be embedded directly into AI development and deployment pipelines.
• Automate standards for evidence collection and documentation to support audit and assurance needs.
• Implement technical guardrails, including usage constraints and access patterns, to ensure safe AI operations.
• Develop systems to provide visibility into the enterprise AI landscape and manage "Shadow AI."
• Establish continuous monitoring and observability mechanisms for AI systems rather than relying on point-in-time assessments.

What we're looking for

• Must have a Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field.
• Must have 8 years of relevant experience with a Bachelor's degree or 6 years with a Master's degree.
• Must possess a Secret security clearance and U.S. citizenship.
• Experience designing, building, or integrating AI/ML systems or data-driven applications in an enterprise environment.
• Experience with data security, privacy, cybersecurity, or related control environments.
• Demonstrated experience translating governance, risk, or compliance requirements into technical controls, automated workflows, or monitoring solutions.
• Hands-on experience implementing technical guardrails or standards for complex systems like access control and audit logging.
• Experience working with cross-functional stakeholders such as Privacy, Legal, Cybersecurity, and Enterprise Architecture.
• Experience designing and operationalizing an enterprise AI Governance Framework across the full lifecycle (preferred).
• Experience developing reusable governance components like templates, SDKs, APIs, or policies-as-code (preferred).
• Experience establishing common taxonomies and classification schemes for AI or data use cases (preferred).
• Experience implementing ongoing monitoring for AI systems and managing Shadow AI (preferred).
• Experience with enterprise GRC platforms like OneTrust (preferred).
• Experience with AI and data platforms such as Databricks or IBM WatsonX (preferred).
• Experience with international AI governance, emerging regulations, and multi-jurisdiction environments (preferred).
• Familiarity with AI safety, model risk management, or Responsible AI practices (preferred).
• Experience in large, complex, or highly regulated industries like defense or aerospace (preferred).
• Experience with cloud platforms and modern data/ML engineering practices such as MLOps or DevSecOps (preferred).