Agentic AI Engineer & GenAI Architect

Booz Allen; Honolulu (HI); On-site; USD 99,000 - 225,000; Full time

Job summary

Booz Allen Hamilton seeks an experienced engineer to design production-grade agentic AI systems, integrating LLMs, RAG pipelines, and multi-provider orchestration to deliver measurable value for clients.

You will build modular components for prompting, evaluation, and deployment, ensure safety and observability, and collaborate with data engineers, solutions architects, and product owners across engagements. Requires 2+ years in software/AI roles and a CS/Engineering degree.

Qualifications

• 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.

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, multi-agent workflows.
• Leverage modern build and packaging workflows to deliver optimized, portable application artifacts.
• Use AIassistancetools to accelerate development, debugging, and system design while maintainingengineering rigor and code quality.
• Collaborate with clients toidentifyhigh-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.

Skills

Software engineering

AI/ML roles

Python

AI applications

LangChain

RAG

Asynchronous programming

Cloud (AWS)

Education

Bachelor’s degree in CS or Engineering

Tools

LangChain

Docker

Kubernetes

Job description

Booz Allen Hamilton seeks an experienced engineer to design production-grade agentic AI systems, integrating LLMs, RAG pipelines, and multi-provider orchestration to deliver measurable value for clients.

You will build modular components for prompting, evaluation, and deployment, ensure safety and observability, and collaborate with data engineers, solutions architects, and product owners across engagements. Requires 2+ years in software/AI roles and a CS/Engineering degree.