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Lead and scale engineering across application, backend, infrastructure, and data teams. Responsibilities include hiring and developing engineers, guiding architecture and system design, driving reliable product execution, establishing engineering standards, shaping platform strategy, and partnering with AI, product, and customer-facing teams. The role also requires maintaining strong reliability, security, and privacy practices for clinical patient data.

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.

Lead end-to-end technical delivery of AI deployments for healthcare customers: set architecture, decompose ambiguous problems into deliverable work, make tradeoffs, manage scope and timelines, work directly with customers, and ensure production-quality LLM/agent systems.

Design, build, and own high-throughput backend and infrastructure services for a healthcare AI agent platform. Ensure reliability, graceful degradation, observability, performance, and secure handling of sensitive patient data. Drive architecture and operate services end-to-end in production.

Build and operate the end-to-end data layer: ingest and sync EHRs and customer systems, transform messy sources into a normalized patient/provider model, maintain data freshness and query performance, and enable natural-language query interfaces and serving for production AI workloads.

Build and own full‑stack applications for clinicians and researchers: real‑time oversight interfaces, schema-to-API-to-UI features, no-code agent behavior tools, and natural‑language exploration of healthcare data. Ensure high correctness, reliability, and performance for systems handling sensitive patient data while collaborating with designers, clinicians, and customers.