Agentic AI Engineer: Build Production GenAI Systems

Booz Allen Hamilton; Waipahu (HI); Sur place; USD 99 000 - 225 000; Plein temps

Résumé du poste

Booz Allen Hamilton is seeking an Agentic AI and Data Engineer to design production-grade agentic AI systems, integrate multiple model providers, and build scalable AI workflows. You will prototype with LangChain, RAG pipelines, tool-calling, and memory mechanisms to deliver measurable value.

You will collaborate with data engineers and product owners, implement evaluation frameworks, optimize for cost and latency, and deploy secure AI services on AWS or similar clouds, with containerization and

Qualifications

• Experience designing production-grade AI/ML applications.
• Experience with agentic AI, prompting, RAG, and tool use.
• Familiarity with LangChain, workflows, and multi-provider models.
• Bachelor’s degree in CS or Engineering.

Responsabilités

• Design adaptable agentic AI architectures with multiple model providers.
• Build modular components for prompting, orchestration, and memory.
• Integrate LLMs, embeddings, and RAG pipelines in production systems.
• Apply advanced prompting, tool-calling, and function-calling techniques.
• Develop evaluation frameworks for accuracy, safety, and business impact.
• Optimize models and workflows for cost, latency, and reliability.
• Create data pipelines for ingestion, embedding, indexing, and refresh for RAG.
• Implement multi-modal processing and cross-modal reasoning.

Connaissances

Python

AI/ML development

Asynchronous programming

DevOps mindset

Formation

Bachelor’s degree in CS or Engineering

Outils

LangChain

Docker

Kubernetes

AWS / Cloud platforms

Description du poste

Booz Allen Hamilton is seeking an Agentic AI and Data Engineer to design production-grade agentic AI systems, integrate multiple model providers, and build scalable AI workflows. You will prototype with LangChain, RAG pipelines, tool-calling, and memory mechanisms to deliver measurable value.

You will collaborate with data engineers and product owners, implement evaluation frameworks, optimize for cost and latency, and deploy secure AI services on AWS or similar clouds, with containerization and