Agent Deployment Architect (Healthcare AI)

TL;DR

Agent Deployment Architect (Healthcare AI): Serve as technical lead for deploying safety-focused LLMs in healthcare environments with an accent on integrating with EHR systems, customizing solutions, and ensuring seamless implementations. Focus on bridging engineering teams with client stakeholders, prototyping integrations, and owning the full deployment lifecycle including validation and troubleshooting.

Onsite role based in Cleveland, OH, five days per week at the client site with occasional travel to Palo Alto, CA. Must satisfy client-specific credentialing including drug screening and vaccinations.

Company

Leading generative AI company in healthcare building the world’s first safety-focused LLM for autonomous clinical patient conversations, backed by top investors at $3.5B valuation.

What you will do

• Serve as technical lead on client engagements, guiding end-to-end solution deployment and customization.
• Act as bridge between engineering team and client stakeholders for smooth implementations.
• Rapidly prototype integrations, build internal tooling, and configure systems to meet client needs.
• Own technical deployment lifecycle: onboarding, configuration, validation, and troubleshooting.
• Partner with product and clinical teams to surface insights and inform product roadmap.

Requirements

• Bachelor’s or Master’s in Computer Science, Engineering, or related field.
• 5+ years in technical customer-facing deployments or implementation engineering.
• Deep experience integrating with healthcare systems like EHRs (Epic, Cerner, Athena), CRMs, population health platforms.
• Strong knowledge of healthcare data standards (FHIR, HL7v2).
• Experience in complex technical environments in health systems, payors, or digital health.
• Excellent communication to translate technical requirements across stakeholders.
• Ability to meet client credentialing: drug screening, vaccinations/immunizations.

Nice to have

• Strong Python skills for debugging and scripting.
• Familiarity with cloud platforms, APIs, secure data practices.
• Startup experience with ambiguity and velocity.