• Design, build, and operate backend services for generative AI capabilities, including LLM-backed APIs, agents, retrieval and knowledge layers, and connectors to internal and external systems. • Build and scale data infrastructure underpinning file management and high-throughput data flows between customer environments, cloud object storage, and compute clusters, with an emphasis on reliability, performance, and cost. • Work across the stack—from backend services (Python/Java/Go) and cloud infrastructure (AWS/Azure/GCP, containers/Kubernetes) to the occasional UI change—to deliver end-to-end user-facing experiences in collaboration with product, design, and other engineering teams.

• Build, maintain, and improve backend services, APIs, and data pipelines that power integrations with external partners and internal systems. • Design and implement scalable, reliable integration patterns for third-party APIs and data exchange. • Ensure the performance, reliability, and accuracy of data flowing across systems. • Debug and troubleshoot issues across distributed systems, integrations, and data pipelines. • Partner with product, operations, and engineering to translate integration requirements into technical solutions. • Participate in system design discussions, code reviews, and team knowledge sharing. • Contribute to improving developer experience through better tooling, documentation, and integration workflows. • Support monitoring, logging, and alerting to proactively identify and resolve issues.

• Build, maintain, and improve backend services, APIs, and data models that power clinician workflows and reporting. • Collaborate with front-end engineers to integrate backend functionality into dashboards, reporting tools, and other clinical applications. • Ensure the reliability, scalability, and performance of data-driven systems. • Participate in system architecture discussions, code reviews, and knowledge sharing. • Debug and troubleshoot issues across backend services and pipelines. • Partner with product and design to translate clinical requirements into technical solutions. • Stay up to date with modern software engineering practices across both frontend and backend development.

• Lead the design and development of our integrations platform, including APIs, data pipelines, and third-party system integrations (e.g., EHRs, partner systems, internal services). • Drive the architecture and implementation of scalable, secure, and reliable integration systems. Establish best practices for API design, data modeling, and system interoperability. • Partner closely with product, design, operations, and external stakeholders to translate integration requirements into technical solutions. Collaborate across US and India-based engineering teams to ensure alignment and execution. • Own the performance, monitoring, and reliability of integration systems. Proactively identify and resolve bottlenecks, data inconsistencies, and failure points. • Improve the experience for both internal teams and external partners by building intuitive, well-documented, and reliable integration workflows and tooling. • Write clean, maintainable, and efficient code while setting standards for the team. Contribute directly to critical parts of the system. • Mentor engineers and foster a high-performing, collaborative team environment. Prepare to transition into a full-time Engineering Manager role within 6–9 months. • Stay current with best practices in distributed systems, APIs, and data integration. Continuously improve system scalability, security, and maintainability.