> Markdown version of [/jobs/ext/1499944-sr-ai-context-engineer](https://www.wearedevelopers.com/jobs/ext/1499944-sr-ai-context-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr AI Context Engineer - **Company:** Government Employees Health Association, Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $124,666.0 - $157,710.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Data Analysis, Microsoft Azure, Computer Programming, Modems, Continuous Integration, Data Cleansing, Data Infrastructure, Data Security, Internet Services, Python (Programming Language), Machine Learning, Metadata, Parsing, Software Product Management, Role-Based Access Control, Search Technologies, Software Engineering, SQL Databases, Enterprise Software Applications, Large Language Models, Database Optimization, Indexer, Backend, Low Latency, Search Engines - **Published:** July 30, 2026 - **Apply:** https://geha.wd5.myworkdayjobs.com/GEHACareers/job/Missouri-Remote/Sr-AI-Context-Engineer_R-005302 ## About the Role * Experience: 5+ years of experience in backend software engineering, applied machine learning/search, or data-centric application development. * GenAI & RAG Focus: 1-2 years of hands-on experience specifically optimizing AI and RAG architectures, prompt context strategies, and vector retrieval pipelines for LLM applications. * Programming & Tooling: Advanced proficiency in Python and SQL, alongside familiarity with modern orchestration tools (e.g., Airflow, Prefect, Temporal). * Unstructured Content Parsing: Deep experience using parsing frameworks (e.g., LlamaIndex, Unstructured.io, LangChain document loaders) to process complex layouts, tables, and unstructured documents. * Vector & Search Engines: Hands-on experience working with vector databases, embeddings, and semantic search platforms (e.g., Pinecone, pgvector, Weaviate, Qdrant, Azure AI Search). * Evaluation Frameworks: Familiarity with RAG and LLM context evaluation frameworks (e.g., Ragas, TruLens, Arize Phoenix) to measure retrieval recall, precision, and groundedness. * Data Security & Privacy: Practical experience handling sensitive healthcare data (PHI/PII) within high-compliance software environments. Work-at-home requirements * Must have the ability to provide a non-cellular High Speed Internet Service such as Fiber, DSL, or cable Modems for a home office. * A minimum standard speed for optimal performance of 30x5 (30mpbs download x 5mpbs upload) is required. * Latency (ping) response time lower than 80 ms ## Description 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., 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 * Healthcare Context Compliance: Ensure all document processing and contextual payloads strictly adhere to HIPAA and HITRUST standards, implementing automated masking and tokenization for Protected Health Information (PHI). * Context-Level Access Control: Ensure role-based access control (RBAC) rules within vector metadata, ensuring AI search queries only return contextual snippets that the active user is authorized to see. * Auditing & Hand-off Lineage: Maintain context tracking and audit logs for prompt payloads, establishing clean data hand-off specifications when transitioning validated innovation prototypes to enterprise Data & Analytics or IT teams. Collaborative Execution * Reference Pattern Alignment: Build upon the reference architectures, CI/CD templates, and "golden paths" established by the Sr. AI Solutions Architect. * Product Support: Work alongside the AI Product Owner and AI Full Stack Developers to rapidly supply high-accuracy context layers for upcoming GenAI features. ## Related Videos - [RAG's Not Dead, You're Just Using It Wrong! - Phil Nash](https://www.wearedevelopers.com/videos/1906-rag-s-not-dead-you-re-just-using-it-wrong-phil-nash) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation](https://www.wearedevelopers.com/videos/1235-carl-lapierre-exploring-advanced-patterns-in-retrieval-augmented-generation) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? 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