Software Architect

What we want to accomplish and why we need you. Suki is a leading technology company that provides AI voice solutions for healthcare. Its mission is to reimagine the healthcare technology stack, making it invisible and assistive to lift the administrative burden from clinicians. Its flagship product is Suki Assistant, an AI assistant that uses generative AI to automatically create clinical documentation by ambiently listening to patient-clinician conversations. Suki helps clinicians complete notes 72% faster on average, assists with other tasks including coding and answering questions, and generates incremental revenue for organizations, delivering a 9X ROI in year 1. Suki also offers its proprietary AI and speech platform, Suki Platform, to partners who want to create best-in-class ambient and voice experiences for their solutions. Clinicians that use Suki already spend over 70% less time on administrative tasks, and we’re striving to do even better. Come and join us! We are a user-driven company and are committed to making sure every pixel of our product is in service of the doctors. We’re a team of technologists, clinicians, and industry experts working together to push the limits on technology used in medicine. We’re confident enough to move fast and talented enough not to break things. What will you do every day? We are looking for a Software Architect to join our Engineering team. Suki's architecture practice is at an inflection point. We have sustained significant CCU growth, completed a demanding OLTP Database scaling journey, leveraged Redis Streams based service orchestration, deployed self-hosted ML models, built agent-driven workflows, launched an Offline Data Platform, and maintained a client surface spanning iOS, Android, Web, SDKs, and a Chrome extension — with on-device ML capabilities across all of them. The Architect is the person who holds institutional memory and architectural coherence across this surface area — so that decisions made in isolation don't quietly contradict each other, patterns are reused rather than reinvented, and deviations from established patterns are documented, not silently absorbed. The work ahead includes enhanced agentic capabilities, continued multi-region expansion, investments on stream-based/offline data processing, and a growing ML inference footprint. On a day to day basis, you will: Maintain architectural coherence across a complex and wide surface area. Own the load-bearing substrate through future growth and failure scenarios. The system must survive several multiples of current enterprise scale and every class of infrastructure event we can name. Drive the structural decomposition of platform and domain boundaries, service boundaries across the microservice portfolio, agentic framework direction, auth and access-control architecture, and cross-cutting concerns like observability and rate limiting. Bridge AI research and production infrastructure. Translate LLM and model requirements into efficient, production-grade services. Lead the agentic framework direction — memory architectures, tool orchestration, A2A patterns, semantic caching. Operate a GPU fleet at high duty cycle and understand dynamic batching, cross-session batching tradeoffs, and HPA calibration for GPU workloads. Document architectural patterns and deviations. Write Architectural Design Records, RFCs, and breaking-points documents that stand alone for readers unfamiliar with the context. Document deviations from established patterns with their reasons — not as failures, but as data points. Remain hands-on — stay connected to the craft, read and write code. Prototype new architectures. Review pull requests for patterns that matter. Implement the hard parts of a new pattern yourself when it matters. Lead resolution of the most critical incidents. Mentor and elevate the engineering organization. Act as a technical multiplier for senior engineers across backend, data, and ML disciplines. Hold a room, defend a p