Enterprise Cybersecurity Solution Engineer

Booz Allen Hamilton • McLean, VA

1d • Onsite

About The Position

As a Cybersecurity Solution Engineer, you will operate as a hands-on solutions integrator and technical leader responsible for designing, configuring, developing, and deploying enterprise cybersecurity operations solutions for use by the Booz Allen’s cyber Operations teams. This role emphasizes execution and delivery of security capabilities, including advanced AI-enabled cybersecurity solutions, while ensuring alignment with enterprise architecture, risk posture, and operational objectives. You will bridge architecture and operations by translating security designs into deployable, scalable, and automated implementations across cloud, network, endpoint, identity, and application domains. You will originate, facilitate, and lead cross-functional efforts to deploy and mature Enterprise Cybersecurity Operations capabilities, including prevention, detection, response, recovery controls, and efficient execution, while guiding teams through threat-informed improvements, security-by-design practices, and architectural remediation of control gaps. You will perform security solution reviews and provide technical direction for complex initiatives, including modernization, cloud adoption, and platform transformation efforts, translating security findings, incident learnings, and threat intelligence into actionable design decisions and measurable implementation plans. You’ll leverage strong analytical and communication skills to assess complex security and business problems, align technical and non-technical stakeholders, and drive decisions to closure in support of Booz Allen’s critical enterprise infrastructure, go-to-market platforms, and mission operations.

Requirements

• 7+ years of experience in cybersecurity engineering, security architecture, or enterprise security solution implementation, including leadership of cross‑domain security initiatives
• Experience designing and implementing enterprise security operations across network, endpoint, application, identity, and cloud environments, with integration across tools using APIs, automation, and workflow orchestration
• Experience applying AI and machine learning to cybersecurity scenarios such as threat or anomaly detection, alert triage, analyst copilots, and response automation, supported by Python-based development for security and AI / ML use cases
• Experience with modern AI / ML frameworks and toolchains, including PyTorch, TensorFlow, scikit‑learn, and Hugging Face, and agent frameworks such as LangChain or LlamaIndex
• Experience operationalizing AI / ML systems (MLOps), including model versioning, experiment tracking, evaluation, drift or quality monitoring, and CI / CD for models
• Experience streamlining and redefining operational processes to eliminate manual steps and improve delivery efficiency
• Knowledge of cloud security architectures and native controls in AWS, Azure, or GCP, vector databases such as pgvector, OpenSearch, Pinecone, or Milvus, and modern cybersecurity threats, including ransom ware, insider threats, credential abuse, data exfiltration, and AI‑enabled attacks such as prompt injection, evasion, poisoning, or model theft
• Knowledge of secure AI implementation practices such as model or data protection, prompt or inference risk mitigation, agent guardrails, or governance aligned to NIST AI RMF, OWASP LLM Top 10, or MITRE ATLAS
• Ability to obtain a Secret clearance
• Bachelor’s degree

Nice To Haves

• Experience with programming or scripting languages used in security and automation environments such as Python, Go, SQL, PowerShell, or Bash
• Experience designing, deploying, and maintaining enterprise-scale security solutions for sensitive or regulated environments such as FedRAMP, IL4 / 5, HIPAA, or PCI
• Experience designing and building agentic AI systems for security operations, including multi-step reasoning, tool or function calling, retrieval pipelines, and human-in-the-loop workflows
• Experience fine-tuning, distilling, or evaluating LLMs and other models for domain-specific security tasks, including building eval datasets and red-teaming AI systems
• Experience evaluating and integrating AI-enabled cybersecurity tooling such as AI-assisted SIEM or SOAR, UEBA or behavioral analytics, or model-driven detection workflows into enterprise security operations
• Knowledge of AI governance, model risk management, and policy controls aligned to enterprise and regulatory expectations for responsible AI use
• Knowledge of data governance frameworks, data classification standards, and privacy regulations such as GDPR or CCPA
• Knowledge of database structures, data modeling fundamentals, and query optimization, including SQL and NoSQL platforms
• IT Engineering or Security Certifications such as CISSP, CCSP, or CDPSE Certification, Cloud Security Certifications, or relevant AI Security Certifications such as ISC2 CAISS or IAPP AIGP Certification

Responsibilities

• Design, configure, and implement enterprise cybersecurity operations solutions across identity, endpoint, network, application, and cloud environments, translating architecture into scalable, production-grade deployments.
• Develop automation, scripting, and Infrastructure-as-Code (IaC) to enable repeatable, testable, and version-controlled security implementations and integrations across platforms.
• Design, build, and deploy custom AI / ML solutions for cybersecurity, including model development, retrieval-augmented generation (RAG) pipelines, agentic workflows, and LLM-assisted analyst tooling.
• Operationalize custom AI / ML solutions end-to-end, including data pipeline, training or tuning, evaluation, deployment, and monitoring.
• Apply secure-AI engineering practices throughout the AI / ML lifecycle, including model and data protection, prompt and inference risk mitigation, evaluation against adversarial inputs, and responsible AI controls.
• Implement and orchestrate security tools and controls such as SIEM, SOAR, EDR, IAM, or CSPM, including detection logic, response playbooks, and cloud-native security policies, and extend them with custom AI / ML capabilities where commercial tooling falls short.
• Collaborate across engineering, platform, data, and operations teams to deliver end-to-end solutions, embed security into DevSecOps and MLSecOps pipelines, and drive implementation through to operational outcomes.

Benefits

• health, life, disability, financial, and retirement benefits
• paid leave
• professional development
• tuition assistance
• work-life programs
• dependent care
• recognition awards program

What This Job Offers

• Job Type: Full-time
• Career Level: Senior