Enterprise Cybersecurity Solution Engineer

Enterprise Cybersecurity Solution Engineer

The Opportunity

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 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, and recovery controls. You will guide 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. This position is located in McLean, VA.

• 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 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 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 Dev Sec Ops  and ML‑Sec Ops pipelines, and drive implementation through to operational outcomes.

Join us. The world can’t wait.

• 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.
• Experience with modern AI / ML frameworks and tool chains, including PyTorch, Tensor Flow, scikit‑learn, and Hugging Face, and agent frameworks such as Lang Chain or Llama Index.
• Experience operationalizing AI / ML systems (MLOps), including model versioning, experiment tracking, evaluation, drift or quality monitoring, and CI / CD for…