Agentic AI Security Engineer, Lead

Booz Allen Hamilton•McLean, VA

About The Position

Booz Allen is seeking a security-focused AI/ML engineer to help customers deploy agentic AI securely and responsibly. The role combines hands-on development, cybersecurity expertise, and architectural thinking to design, build, and evaluate secure AI applications and agentic workflows. The engineer will analyze interactions among models, orchestration, tools, data sources, and enterprise infrastructure; identify risks; and develop practical controls across the system lifecycle. The position turns emerging research and complex technical requirements into working prototypes, reusable architecture patterns, and technical guidance. It involves collaboration with AI engineers, cybersecurity specialists, architects, and mission stakeholders to balance capability, security, and operational needs, and to help technology leaders and implementation teams move from experimentation to secure, governed AI deployment. The Lead provides day-to-day technical leadership, conducts design reviews, guides architectural decisions, and raises the team's technical standards.

Requirements

8+ years of experience in software engineering or AI/ML engineering, including developing and deploying AI/ML systems.
Experience developing in Python and applying software engineering practices, including Git, automated testing, code review, and dependency management.
Experience building applications using large language models, including tool use, retrieval-augmented generation, and agent orchestration.
Experience designing or evaluating system architectures involving APIs, data pipelines, model services, and enterprise integrations.
Experience applying cybersecurity principles to software or AI systems, including identity and access management, trust boundaries, secure data handling, and threat modeling.
Experience with traditional machine-learning approaches and algorithms and their applications to cybersecurity.
Knowledge of NIST security and risk management frameworks, including SP 800-53 and the AI Risk Management Framework, and Zero Trust Architecture principles, including identity-based access, least privilege, and continuous verification.
Knowledge of AI-specific security risks and approaches for evaluating and mitigating vulnerabilities in generative AI and agentic systems.
Ability to obtain a Secret clearance.
Bachelor’s degree in computer science, mathematics, engineering, or cybersecurity.

Nice To Haves

Experience developing agentic AI applications using frameworks or platforms such as Strands Agents, LangGraph, Amazon Bedrock, GraphRAG, or comparable technologies, including integrating agents with workflow orchestration tools such as Temporal.
Experience securing tool integrations using Model Context Protocol or other agent communication and integration mechanisms.
Experience conducting AI red teaming, adversarial testing, or automated security evaluations of models and agentic workflows.
Experience implementing AI gateways, policy enforcement, execution sandboxes, agent observability, or human-in-the-loop controls.
Experience deploying AI applications in cloud or on-premises environments using containers, CI/CD pipelines, and Infrastructure-as-Code.
Experience with model training, fine-tuning, evaluation, or serving using frameworks such as PyTorch or comparable technologies.
Experience with distributed computing, data engineering, feature pipelines, or relational and vector databases.
Experience applying NIST guidance and Zero Trust Architecture principles to AI systems and translating security requirements into architectural controls, implementation patterns, and assessment evidence.
TS/SCI clearance.

Responsibilities

Design and validate secure architectures for generative AI applications, AI agents, and multi-agent systems, covering model access, orchestration, tool execution, memory, and retrieval-augmented generation.
Develop prototypes and reference implementations that demonstrate security controls and evaluate architectural tradeoffs.
Assess AI systems for risks including prompt injection, sensitive data exposure, excessive permissions, unsafe tool execution, and compromised integrations.
Design and implement controls for agent identity, least-privilege access, execution isolation, tool authorization, data protection, and human approval of consequential actions.
Develop architecture diagrams, data flow diagrams, trust-boundary models, and technical documentation explaining system behavior, control placement, and implementation requirements.
Establish evaluation and monitoring approaches for agent behavior, security, reliability, and policy compliance, including traceability across model interactions and tool calls.
Translate security and governance requirements into engineering practices, including testing, version control, change management, audit trails, and rollback procedures.
Evaluate emerging models, agent frameworks, integration protocols, and security tools, and communicate findings and recommendations to technical and non-technical audiences.
Provide day-to-day technical leadership to engineers, conduct design reviews, guide architectural decisions, and raise the technical bar of the team.