Agentic AI and Data Engineer

Booz Allen Hamilton•Honolulu, HI

7d•$99,000 - $225,000

About The Position

Agentic AI and Data Engineer The Opportunity: As an experienced engineer, you know how to design, develop, and deliver production-grade agentic AI systems that demonstrate the practical value of generative AI, large language models (LLMs), and autonomous workflows. This role combines deep technical expertise with strong product and consulting skills to design AI applications that leverage prompting, retrieval-augmented generation (RAG), agentic orchestration, evaluation pipelines, and human-in-the-loop systems to deliver measurable impact. You will architect modular, reusable AI application patterns, integrate multiple model providers such as cloud-hosted, local, and hybrid, and apply modern GenAI stack capabilities, including structured prompting, tool use, workflow orchestration, and multi-modal reasoning. You will design solutions deployable across various contexts, from cloud-hosted platforms to portable, self-contained builds, optimizing for latency, cost efficiency, observability, and safety. You will rapidly prototype and iterate using AI-assisted development tools, validating hypotheses through eval-driven development and continuous experimentation. In this role, you’ll define the direction of mission-critical agentic systems by selecting and combining prompting strategies, RAG architectures, agentic workflows, and fine-tuned or foundation models as appropriate. You’ll be part of a large community of AI and ML engineers across the company, collaborating with data engineers, data scientists, solutions architects, and product owners to deliver world-class solutions. What You’ll Do: 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. Join us. The world can’t wait.

Requirements

5+ years of experience with software engineering
2+ years of professional experience in AI or ML-focused roles
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 using LLMs, LAMs, and LVMs, applying prompting techniques and structured outputs to improve reliability and determinism, and building secure and scalable deployments on cloud platforms such as AWS
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

Nice To Haves

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 consulting 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

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.