Defensive Cyber Analytic Solution Engineer

Booz Allen Hamilton is a leading consulting firm that focuses on delivering advanced technology solutions. They are seeking a Defensive Cyber Analytic Solution Engineer to help clients leverage data for critical missions, including fraud detection and national intelligence, by building and maintaining scalable data platforms.

Responsibilities:

• Help our clients find answers in their data to impact important missions—from fraud detection to cancer research to national intelligence
• Build advanced technology solutions and implement data engineering activities on some of the most mission-driven projects in the industry
• Deploy and develop pipelines and platforms that organize and make disparate data meaningful
• Work with and guide a multi-disciplinary team of analysts, data engineers, developers, and data consumers in a fast-paced, agile environment
• Manage the assessment, design, building, and maintenance of scalable platforms for your clients

Requirements:

• 5+ years of experience in cybersecurity, systems architecture or software engineering, or AI/ML solution design
• 4+ years of experience supporting DoD, Intelligence Community, or federal civilian missions
• 2+ years of experience designing, deploying, or securing AI/ML pipelines, such as training, inference, or monitoring
• Experience with LLM platforms and APIs, such as OpenAI, Anthropic, Azure Open AI, or AWS Bedrock
• Experience with Machine Learning frameworks, such as TensorFlow, PyTorch, Scikit-Learn, or Hugging Face
• Experience with Machine Learning orchestration and serving platforms, such as SageMaker, Vertex AI, MLflow, or Kubeflow
• Experience implementing AI security controls, such as data provenance, model integrity, and supply chain, and integrating AI into operational workflows, such as SOC automation, threat detection, and analytics
• Knowledge of AI governance, DoD Responsible AI, and federal compliance
• TS/SCI clearance
• HS diploma or GED
• Experience operating in IL5, IL6, or IL7 environments
• Experience developing AI-enabled defensive cyber capabilities, such as automated triage, detection engineering, or autonomous response
• Experience securing large language models, RAG architectures, or agentic AI systems
• Experience with MLOps or AIOps practices, including model versioning, drift detection, and performance monitoring
• Experience with stream processing platforms, such as Cribl, Kafka, or Flink for real-time AI data ingestion
• Experience with data governance for AI training data in multi-classification or coalition environments