GDMS Senior AI Governance & Risk Specialist

Sr. AI Governance & Risk Specialist

GDMS operates one of the largest enterprise AI deployments in the defense industry not as a pilot program, not as a proof of concept, but deeply embedded in how our workforce operates every day. Adoption is broad, active, and accelerating, spanning generative AI copilots, LLM-powered applications, and a rapidly growing portfolio of agentic and autonomous AI systems. The governance challenge is not getting people to use AI it is keeping pace with a workforce that already does, while ensuring every deployment meets the risk, security, and compliance standards that mission-critical defense work demands.

As a Sr. AI Governance & Risk Specialist, you will be a core practitioner on the Agentic AI Governance team, executing the day-to-day work that keeps GDMS AI deployment safe, accountable, and trusted. You will conduct and lead AI risk assessments, including real-time risk evaluation for active and in-flight deployments, perform governance audits, evaluate and ensure adherence to government and corporate AI regulations, lead implementation of corrective actions, and serve as a subject matter expert for engineering and program teams navigating the AI lifecycle.

You will work directly with agentic tools and applications, bringing firsthand understanding of how they behave, where they fail, and what governance controls actually matter in practice.

This role requires a blend of technical literacy and governance discipline. You do not need to be a researcher or model trainer, but you must understand how AI systems work well enough to assess risk with precision rather than reflexive caution. The right candidate has used agentic tools hands-on, can evaluate an agentic workflow for failure modes, and can translate a NIST AI RMF control into a practical check a program team can execute.

You will coordinate with Legal, Privacy, Business Unit leads, AI Reliability Engineering and the Cybersecurity organization to keep GDMS AI velocity ahead of the market without accumulating unacceptable risk. This is foundational work that bridges policy, risk, and technical implementation, requiring sound judgment, the ability to make independent stakeholder judgment calls, and direct accountability for the recommendations you put forward.

Key Responsibilities

• Conduct and lead comprehensive AI risk assessments and governance audits against emerging regulations for generative AI, LLM-based, and agentic applications; document findings, risk ratings, and mitigation strategies, and lead the implementation of corrective actions.
• Evaluate and ensure adherence to government and corporate AI policies, standards, and regulations across the six layers: AI inventory and discovery; data governance; security and access controls; model assurance; human oversight; and compliance and audit.
• Apply and maintain tiered governance frameworks calibrated to risk level, ensuring low-risk use cases clear quickly while mid- and high-risk applications receive appropriate scrutiny and escalation.
• Maintain the enterprise AI use inventory and control framework, including system inventory, risk register, shadow AI detection, approved use catalog, and control mapping, with accurate and current governance tracking; support dashboard reporting and KPI monitoring for AI governance program health.
• Prepare governance recommendations for approval and escalation, ensuring mid- and high-risk AI systems are escalated with clear risk rationale and decision support materials.
• Support development of self-service governance tooling, checklists, and playbooks that enable program teams to adopt AI responsibly without requiring individual review for low-risk applications.

Agentic AI Risk & Technical Assessment

• Assess risks specific to agentic AI systems and multi-agent architectures including tool-calling behavior, memory and retrieval systems, external API access, autonomous decision loops, and agent-to-agent communication patterns.
• Apply failure mode analysis to evaluate behavioral boundaries, unintended action risks, adversarial prompt vulnerabilities, and out-of-scope execution risks for agentic deployments.
• Evaluate and document…