AI Architect; FDE

We're looking for an AI Architect with Forward Deployed Engineer (FDE) / Agentic AI experience — embedding directly with engineering, technology and product teams as well as client-facing healthcare operations' leaders to design and implement agentic AI systems that solve real hospital pharmacy problems in production. This is a hands‑on architecture role for someone who can move fluidly between enterprise architecture governance and shoulder-to-shoulder build work with delivery pods.

We're looking for an AI Architect with Forward Deployed Engineer (FDE) / Agentic AI experience — embedding directly with engineering, technology and product teams as well as client‑facing healthcare operations' leaders to design and implement agentic AI systems that solve real hospital pharmacy problems in production. This is a hands‑on architecture role for someone who can move fluidly between enterprise architecture governance and shoulder‑to‑shoulder build work with delivery pods.

You will be responsible for assessing and modernizing the product suite with an AI native architecture. Product stack includes CPS (Comprehensive Pharmacy Solutions), operational performance management, contract management, Telepharmacy, risk modeling, purchasing, supply chain and others across GCP, AWS or Azure, with technologies like Java, python and Angular.

• Own the Agentic AI & FDE Delivery Model:
  Embed with CPS product, engineering, technology, data and client‑facing teams as a forward‑deployed architect — translating hospital pharmacy pain points (drug cost variance, compliance gaps, formulary and specialty pharmacy strategy) into deployable agentic AI solutions, iterating in‑context rather than handing off specs.
• Architect Agentic Systems for the CPS Optimizer Platform:
  Design multi‑agent and single‑agent LLM architectures (orchestration, tool‑calling, retrieval, human‑in‑the‑loop escalation) that extend CPS Optimizer's predictive and prescriptive capabilities across drug cost containment, 340B optimization, and operational efficiency use cases.
• Deploy Production‑Grade GenAI & RAG:
  Build enterprise‑ready RAG pipelines over regulatory guidance (340B program rules, CMS/state compliance requirements), drug pricing/formulary data, and client contract terms — enabling accurate, auditable, real‑time recommendations for pharmacy directors, compliance teams, and CPS client success leads.
• Modernize legacy Pharmacy Systems:
  Integrate cloud‑native agentic AI stacks with health‑system pharmacy information systems, 340B split‑billing/TPA platforms, and specialty pharmacy workflows across a diverse base of 800+ hospitals and IDNs — without compromising reliability, uptime, or regulatory compliance.
• Set the Enterprise Reference Architecture:
  Define and evolve architectural patterns for LLM evaluation, responsible AI guardrails, PHI/PII data handling, model governance, and vector store integrations across CPS — serving as the architectural authority other engineering and data science pods build against.
• Rapid Prototyping to Regulated‑Scale Production:
  Move quickly from pilot concepts co‑built with client pharmacy and compliance stakeholders to hardened, production‑grade systems with full observability, audit trails, security compliance (HIPAA, SOC 2, HITRUST), and high availability across a multi‑tenant hospital client base.
• Advise Executive & Client Stakeholders:
  Serve as the technical bridge between CPS leadership, product, data science, and hospital/health‑system client stakeholders — communicating architectural trade‑offs, AI risk/safety posture, and roadmap implications in terms non‑technical stakeholders can act on.

• Enterprise & Solution…