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Lead and scale the Applied AI and deployment teams that implement LLM-based agent systems for healthcare customers. Own hiring, team development, deployment playbooks, reference architectures, customer outcomes, and cross-functional partnership with product and growth to ensure adoption, quality, and expansion inside health systems.

The Senior Software Engineer will build and maintain high-performance backend services and APIs for an AI healthcare platform, focusing on concurrency and reliability while ensuring secure data handling and system scalability.

As a Senior Full-Stack Engineer, you will build applications for clinical operations, design APIs, implement real-time interfaces, and collaborate with teams on patient data management.

As the first sales hire, you'll establish enterprise sales, generate leads, close large deals, create sales strategies, and represent Amigo at events.

The AI Deployment Strategist will collaborate with healthcare organizations to implement AI solutions, managing customer relationships and ensuring value realization throughout the deployment lifecycle.

Design and own platform systems powering AI agents for healthcare. Architect reliable, safe production systems; solve reliability, performance, and data problems end-to-end; set engineering patterns and standards; make platform-level tradeoffs; and mentor senior engineers while shipping ambiguous, high-impact projects.

Design and own platform-level systems that power reliable, safe AI agents in healthcare. Define architecture and standards, solve ambiguous reliability/performance/data problems end-to-end, make build vs buy decisions, and mentor senior engineers while ensuring systems operate robustly in production.

Build and operate high-throughput, reliable backend services for an AI-driven healthcare agent platform. Own services end-to-end, ensure availability and graceful degradation, handle sensitive patient data, improve performance and observability, and shape platform architecture.

Build and own end-to-end full-stack features for clinical tools: real-time oversight, natural-language healthcare data exploration, no-code agent design tools, and reliability/performance improvements for systems handling sensitive patient data.

Lead end-to-end technical delivery of AI deployments for healthcare customers: define architecture, decompose ambiguous problems into deliverable work, make tradeoffs, manage timelines and quality, grow engineers, and interface directly with customers to keep scope and production standards honest.