Sr AI Context Engineer

Government Employees Health Association, Inc. (G.E.H.A) is a nonprofit member association that provides health and dental benefits that millions of federal employees and retirees, military retirees and their families have counted on since 1937. Offering one of the largest health and dental benefit provider networks available to federal employees in the United States, G.E.H.A empowers health and wellness by meeting its members where they are, when they need care. G.E.H.A has one mission: To empower federal workers to be healthy and well.

As a Senior AI Context Engineer in Digital Innovation, you turn raw, structured and unstructured enterprise information—such as policy documents, brochures, and clinical records—into accurate, high-quality context for AI applications. Positioned at the intersection of applied data science and software engineering, you focus on the quality, semantic structuring, and retrieval accuracy of data consumed by G.E.H.A’s AI solutions. You own the extraction, document chunking, vector indexing, and RAG retrieval mechanics that power G.E.H.A’s AI tools. Operating closely with the Sr.

AI Solutions Architect, AI Full Stack Developer, and enterprise partners, you serve as the contextual bridge between the enterprise Data & Analytics, Digital Innovation and enterprise applications. In this role, you establish data enrichment, retrieval and evaluation frameworks that ensure AI agents have fast, secure, and compliant access to business context, all while leveraging enterprise cloud and data infrastructure.

SKILLS Duties and Responsibilities: Context Engineering & Retrieval Optimization Extraction & Chunking: Build and refine advanced structured and unstructured information parsing, layouts processing, and chunking workflows (converting PDFs, clinical notes, data, and policy docs) into high-quality contextual units for LLMs in partnership with cross-functional teams. Semantic Search & Reranking: Implement hybrid search mechanics, metadata routing, and reranking logic to drastically improve retrieval precision and minimize model hallucinations.

Agentic Context Services: Design context payload specifications and metadata structures that feed into Model Context Protocol (MCP) servers and LLM orchestration tools built by application developers. Vector Indexing & Retrieval Architecture Index Design & Optimization: Recommends and implements vector indexing strategies, embedding schemes, and semantic query designs inside enterprise-provisioned vector stores (e.g., Pinecone, pgvector, Azure AI Search) to ensure high-performance, low-latency retrieval. Vector Metadata: Design and manage sophisticated metadata tagging schemes to enable precise filtering, hybrid search, and domain-specific context retrieval.

Retrieval Evaluation & Groundedness: Establish automated evaluation frameworks to continuously monitor retrieval relevance, context quality, groundedness scores, and embedding drift over time. Context Security, Compliance & Governance…

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