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.

As the Senior AI Solution Architect within the Digital Innovation team, you will be the technical visionary responsible for designing, integrating, and scaling Artificial Intelligence and Machine Learning applications and solutions. While the AI Product Owner defines what we build for our members and business, you will define how we build, deploy, and manage AI application patterns securely.

You will lead the architectural design of scalable AI/ML solution patterns, enabling the rapid development of tools and applications, insights and reporting, Generative AI, and automation solutions. Operating at the intersection of AI engineering, solution strategy, and developer experience, you will ensure our AI solutions act as a force multiplier, are robust, HIPAA-compliant, and capable of supporting GEHA’s strategic modernization directives in collaboration with enterprise IT infrastructure, security and data teams.

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…