Booz Allen Hamilton

Agentic AI and Data Engineer

Honolulu, HI, US$99,000-$225,000Posted 23 days ago

Job Description

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 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-...

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