Sr AI Solution Architect

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.

SKILLS

Duties and Responsibilities:

Enterprise AI Architecture

• Define and maintain enterprise AI application reference architecture, including agent patterns, RAG pipelines, orchestration frameworks, and hosted innovation environments built on top of enterprise-provisioned cloud infrastructure.
• Partner to establish and inform MCP-based integration strategy, including promotion criteria, approved patterns, and lifecycle standards for AI-connected services.
• Own the architectural blueprint for how AI capabilities are composed, exposed, and operationalized.
• Drive AI FinOps best practices by designing cost-effective LLM integration patterns, optimizing token usage, caching strategies, and managing compute resources across cloud environments in partnership with IT Ops.
• Lead "Build vs. Buy" technical evaluations for AI capabilities, defining the architectural integration patterns for third-party AI SaaS tools versus internally hosted models.

Platform & Developer Experience

• Helps shape the AI developer experience end-to-end: golden paths, CI/CD templates, reference repositories, prompt libraries, and evaluation frameworks.
• Ensure that well-architected AI solutions are also the path of least resistance, reducing friction for teams adopting AI-native patterns.
• Prevent Digital Innovation from becoming a collection of bespoke, fragile implementations by establishing reusable, composable building blocks.
• Lead the technical execution of rapid Proof of Concepts (PoCs) to validate emerging AI frameworks and architectural patterns before IT Ops handoff or enterprise-wide adoption.

Cross-Functional Enablement

• Work alongside core cloud platform and data & analytics teams as a peer and partner, shaping AI-layer decisions without owning underlying infrastructure.
• Serve as the primary conduit between Digital Innovation and Security, IT Ops, and Compliance teams, translating AI application needs into governance-compatible designs.
• Architect solutions that strictly adhere to healthcare compliance standards (e.g., HIPAA, HITRUST), ensuring secure handling, anonymization, and encryption of Protected Health Information (PHI) in AI workflows.
• Establish technical guardrails for AI Governance, including mechanisms for model explainability, hallucination mitigation, and algorithmic bias detection.
• Collaborate with senior leadership to align AI application investments with organizational strategy and enterprise risk posture.

Technical Leadership & Community

• Function as a technical authority across AI domains: LLM integration, agentic systems, vector search, model evaluation, and AI observability.
• Mentor engineers and architects in AI-native design patterns, responsible AI practices, and reusable/composable thinking.
• Stay at the leading edge of the AI ecosystem: evaluating emerging tools, frameworks, and standards (e.g., MCP, LangGraph, emerging agent protocols) and translating them into actionable guidance.

Knowledge, Skills, and Abilities:

• Accredited degree in Computer Science, Information Systems, Computer Engineering, or a closely related field, or equivalent experience.
• 5+ years of software engineering experience required, with demonstrated progression into architecture or platform-related responsiblities.
• 3+ years of hands-on experience with AI/ML systems, including LLM integration, RAG architectures, and agentic workflows.
• Deep familiarity with AI orchestration frameworks (e.g., LangChain, LangGraph, Semantic Kernel, AutoGen) and emerging agent interoperability standards such as MCP.
• Strong knowledge of vector databases and semantic search (e.g., Pinecone, pgvector, Weaviate, Azure AI Search).
• Experience designing and publishing reusable engineering patterns, reference architectures, and golden-path templates at an enterprise scale.
• Strong multi-cloud fluency across GCP, AWS, and Azure, particularly AI/ML platform services (Vertex AI (Google Enterprise), Bedrock, Azure OpenAI).
• Demonstrated experience with CI/CD pipelines, DevOps tooling, and platform engineering principles.
• Experience working with Security, IT Ops, and Compliance stakeholders to translate architectural decisions into governance-compatible implementations handling sensitive data (HIPAA/PHI).
• Healthcare industry experience strongly preferred.

Preferred Qualifications

• Experience with Model Context Protocol (MCP) server design, hosted tooling environments, or AI gateway patterns.
• Familiarity with AI evaluation frameworks, prompt management systems, and AI observability tooling.
• Background in developer experience (DX) design: inner-loop tooling, SDK design, or developer portal strategy.

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

• Hotspots, satellite and wireless internet service is NOT allowed for this role.

• A dedicated space lacking ongoing interruptions to protect member PHI / HIPAA information