below marketmost similar roles pay hereabove market
This role pays less than
68%
of similar roles. Most pay
$157,200–$221,800
— the blue band above.
At the midpoint, this role pays about
$162k
versus about
$190k
for comparable roles.
Based on 240 similar postings.
Employer
About Booz Allen Hamilton
Booz Allen Hamilton is a management and technology consulting firm that provides analytics, digital, engineering, and cybersecurity solutions primarily to U.S. government agencies and commercial clients. Industry: Management & Technology Consulting
Booz Allen Hamilton currently has
1662 open roles
on FindRole.
Listed pay typically runs
$86,800–$198,000
across 904 roles with salary data.
The Agentic AI Security Engineer, Lead joins a team focused on designing, building, and evaluating secure AI applications and agentic workflows. This role involves developing secure architectures for generative AI, multi-agent systems, and retrieval augmented generation while identifying risks like prompt injection and sensitive data exposure. You will build prototypes, establish evaluation and monitoring approaches for agent behavior, and translate governance requirements into engineering practices. Key technical requirements include proficiency in Python, Git, and large language models, alongside experience with APIs, data pipelines, and cybersecurity principles like identity and access management. You must understand NIST security frameworks, Zero Trust Architecture, and AI-specific vulnerabilities. The work involves creating trust boundary models and providing technical leadership to engineers to move AI from experimentation to secure, governed deployment.
Design and validate secure architectures for generative AI applications, agentic workflows, and multi-agent systems.
Develop prototypes and reference implementations to demonstrate security controls and evaluate architectural tradeoffs.
Assess AI systems for risks including prompt injection, sensitive data exposure, and unsafe tool execution.
Implement controls for agent identity, least privilege access, execution isolation, and data protection.
Create architecture diagrams, data flow diagrams, and technical documentation explaining system behavior and control placement.
Establish evaluation and monitoring approaches for agent behavior, security, reliability, and policy compliance.
Translate security and governance requirements into engineering practices like automated testing and audit trails.
Provide technical leadership by conducting design reviews and guiding architectural decisions for the engineering team.
What we're looking for
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 like 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, and threat modeling.
Experience with traditional ML approaches and algorithms and their applications to cybersecurity.
Knowledge of NIST security and risk management frameworks, AI Risk Management Framework, and Zero Trust Architecture principles.
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 CS, Mathematics, Engineering, or Cybersecurity.
Experience developing agentic AI applications using frameworks like LangGraph or Amazon Bedrock (preferred).
Experience securing tool integrations using Model Context Protocol (preferred).
Experience conducting AI red teaming, adversarial testing, or automated security evaluations (preferred).
Experience implementing AI gateways, policy enforcement, execution sandboxes, or human-in-the-loop controls (preferred).
Experience deploying AI applications in cloud or on-premises environments using containers, CI/CD pipelines, and Infrastructure-as-Code (preferred).
Experience with model training, fine-tuning, evaluation, or serving using frameworks such as PyTorch (preferred).
Experience with distributed computing, data engineering, or relational and vector databases (preferred).