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Location: United States – Remote Clearance: Ability to obtain and maintain a Public Trust LTS is seeking a highly skilled Agentic AI Security Engineer to ensure our AI systems are secure, trustworthy, resilient, and governed responsibly. You'll design safeguards that protect AI agents, retrieval pipelines, prompts, tools, memory, and model interactions from misuse while ensuring every AI response remains explainable, auditable, and aligned with enterprise security and compliance requirements. This role focuses on securing AI systems, not simply securing infrastructure. The Agentic AI platform is designed to help engineers understand, analyze, and modernize one of the most consequential legacy software systems still operating today. Our platform enables engineers to ask questions in plain English and receive explainable, verifiable answers traced directly back to decades of production source code. Rather than replacing engineers, we're building AI that accelerates engineering through transparency, traceability, and intelligent reasoning. We're building an AI-native engineering platform supporting the modernization of mission-critical healthcare systems serving millions of Veterans nationwide. The platform is designed for deployment across federal enterprise environments and is being engineered to align with FedRAMP security controls, Zero Trust principles, and federal compliance requirements. You'll work alongside AI architects, software engineers, platform engineers, cybersecurity engineers, and product leaders to embed AI security into every stage of development from experimentation through production deployment. We’re building an AI platform where security, transparency, and trust are foundational, not afterthoughts. What You’ll Do: Secure Agentic AI Systems Design and implement security controls for autonomous and multi-agent AI systems. Secure agent orchestration, tool execution, memory, and external integrations. Identify and mitigate emerging AI-specific attack vectors and vulnerabilities. Protect AI Models and Knowledge Systems Secure Retrieval-Augmented Generation (RAG) pipelines, embeddings, vector databases, and enterprise knowledge repositories. Design controls that prevent unauthorized knowledge access, data leakage, and information exposure. Implement secure handling of sensitive enterprise and healthcare data throughout AI workflows. AI Threat Modeling Perform threat modeling for AI applications, agent architectures, prompts, APIs, and retrieval systems. Assess risks associated with prompt injection, jailbreak attempts, indirect prompt attacks, tool misuse, hallucinations, data poisoning, model abuse, and adversarial inputs. Develop mitigation strategies that reduce AI-specific security risks while maintaining usability. Responsible AI & Governance Build guardrails that improve trustworthy AI behavior. Design policy enforcement, human-in-the-loop approval workflows, content filtering, and AI governance mechanisms. Help define organizational standards for responsible AI development and deployment. AI Observability & Monitoring Design monitoring capabilities that detect abnormal agent behavior, misuse, prompt manipulation, and anomalous model interactions. Implement logging, traceability, and audit capabilities supporting explainability and regulatory compliance. Build mechanisms for continuous AI risk assessment and operational visibility. Secure AI Development Partner with software engineers to integrate AI security into development workflows. Conduct security reviews of AI features before production deployment. Promote secure AI engineering practices across the product organization. What We’re Looking For: Bachelor's degree in Computer Science, Cybersecurity, Artificial Intelligence, Software Engineering, Information Security, or a related technical discipline (or equivalent profes