• Design, develop, and maintain core application logic using C# • Build and support Windows desktop applications using WinForms • Develop and optimize SQL Server database objects including stored procedures, queries, and schema design • Troubleshoot and resolve complex production issues across database, application, and UI layers • Work within and improve complex legacy systems while maintaining production stability • Contribute to ongoing enhancements and modernization of existing client/server applications • Collaborate with QA, Product, and cross-functional teams to deliver high-quality releases • Participate in code reviews and contribute to improving development standards and practices • Support testing efforts and improve deployment and release processes • Perform other duties as assigned

• Directly own the core application architecture that connects AI systems to real-world user experiences. • Responsible for the design, reliability, and evolution of backend services, APIs, and client integrations across mobile and desktop. • Ensure production-grade reliability: observability, monitoring, retries, fallbacks, and failure handling. • Ensure AI capabilities are integrated into the product with clear abstractions, predictable behavior, and graceful failure modes.

• Own how this system behaves on desktop environments. • Build and maintain cross-platform desktop applications using Electron. • Design responsive and scalable UIs for real-time collaboration. • Implement desktop-specific functionality including file system access, native notifications, auto-updates, and deep linking. • Integrate AI-powered features (chat, agents, AI assistance) via backend APIs. • Optimize startup time, memory usage, and runtime performance. • Profile and reduce Electron overhead. • Ensure smooth real-time updates (messages, typing indicators, presence).

• Build end-to-end product features across frontend, backend, and AI integrations • Design agent workflows that handle planning, tool use, failure, and recovery across multiple steps. • Integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions • Design real-time AI interactions with streaming, partial results, and tight latency constraints • Improve system reliability, observability, and fallback mechanisms • Collaborate closely with ML, backend, and product teams to ship features end-to-end • Continuously iterate based on real usage and failure modes