Agentic AI and Data Engineer

Booz Allen Hamilton is seeking an experienced Agentic AI and Data Engineer to design and deliver production-grade AI systems that leverage generative AI and large language models. The role involves architecting AI applications, collaborating with a community of engineers, and optimizing solutions for various deployment contexts.

Responsibilities:

• Design adaptable agentic AI architectures that support multiple model providers, tool ecosystems, modalities, and deployment modes
• Build modular and reusable components for prompting, retrieval, orchestration, tool execution, memory management, and evaluation to enable rapid development of new AI capabilities
• Integrate LLMs, embeddings, RAG pipelines, structured outputs, and long-context or memory mechanisms into production-ready systems
• Apply advanced prompting techniques such as few-shot, chain-of-thought, tool-calling, and function-calling, orchestration frameworks such as LangChain or equivalent, and agentic architectures such as MCP, A2A, or similar patterns, to enable goal-directed autonomy with guardrails, observability, and human oversight, including planning, tool use, delegation, and recovery from failure
• Design and implement evaluation frameworks, both offline and online, to measure correctness, robustness, safety, and business impact of AI systems
• Optimize models and workflows for cost, latency, reliability, and scalability, using systematic benchmarking and experimentation
• Develop data pipelines for ingestion, cleaning, chunking, embedding, indexing, and continuous refresh of structured and unstructured data for RAG and memory systems
• Combine text, audio, vision, and other modalities in unified processing workflows, including document understanding, transcription, summarization, and cross-modal reasoning
• Leverage vector databases, hybrid search, reranking, and retrieval optimization techniques to enhance grounding and reduce hallucination in RAG systems
• Incorporate guardrails, safety filters, access controls, and monitoring mechanisms to ensure responsible and secure deployment of agentic AI systems
• Deploy AI services securely and at scale on AWS or equivalent cloud platforms
• Use containerizing, including in Docker or Kubernetes, or serverless approaches for flexible deployment
• Apply CI/CD and eval-driven development best practices for AI systems, including automated testing of prompts and workflows, versioning of prompts and agents, and safe rollout of model updates
• Use asynchronous programming and event-driven patterns to support scalable, long-running, or multi-agent workflows
• Leverage modern build and packaging workflows to deliver optimized, portable application artifacts
• Use AI assistance tools to accelerate development, debugging, and system design while maintaining engineering rigor and code quality
• Collaborate with clients to identify high-value AI opportunities and define solution requirements
• Present AI capabilities and technical solutions to both technical and non-technical stakeholders
• Lead workshops and prototyping sessions to accelerate adoption
• Provide guidance on responsible AI practices, ethics, and compliance

Requirements:

• 2+ years of experience with software engineering
• 2+ years of experience in AI or ML-focused roles in a professional work environment
• Experience with an object-oriented programming language such as Python, and applying it to AI/ML solution development
• Experience designing and implementing production-grade generative or agentic AI applications
• Experience with AI orchestration frameworks such as LangChain, agent workflows, tool integration, and multi-provider model integration
• Experience with RAG architectures, evaluation methodologies, experimentation workflows, and asynchronous or event-driven programming patterns
• Knowledge of data processing techniques for AI, including text, audio, and multi-modal
• Ability to obtain a Secret clearance
• Bachelor's degree in a CS or Engineering field
• Experience with agent frameworks, interoperability standards, and multi-agent patterns such as MCP, A2A, LangGraph, or equivalent
• Experience with model fine-tuning, prompt tuning, domain adaptation, or reinforcement learning from human or AI feedback
• Experience designing evaluation suites or safety testing frameworks for AI systems, and integrating AI systems with external tools, APIs, or enterprise systems via tool-calling or computer-use patterns
• Experience delivering AI solutions in client-facing engagements
• Experience with modern front-end libraries and frameworks for component-based UI development, including React, and with workflows such as build pipelines, automated testing, and code quality tooling
• Experience with in-browser or edge AI execution and performance optimization techniques, as well as modern build and packaging approaches for portable or offline-capable applications
• Experience with developer productivity tools such as Cursor and Windsurf
• Secret clearance
• Master's degree in CS, AI, or a related field
• AWS Machine Learning, Data Engineer, or Solutions Architect Certification