• Collaborate with the team to design, develop, and improve existing systems • Implement new features and enhance current platform functionality • Develop solutions supporting new products and user experiences • Maintain production systems and investigate root causes of issues • Actively participate in Agile/Scrum ceremonies • Ensure adequate unit and integration test coverage • Contribute to code reviews and technical discussions • Support architecture planning and system design activities • Communicate effectively and collaborate with distributed teams

• Develop and maintain robust Python applications • Collaborate with cross-functional teams to define and design new features • Participate in code reviews and enforce best practices • Troubleshoot and debug applications to optimize performance

• Build the core backend services: Design, implement, test, and operate services for context ingestion and indexing. • Create a scalable SaaS foundation: Define and build architecture for multi-tenant service. • Power agent-facing retrieval workflows: Build APIs for AI agents and internal applications. • Work across product and infrastructure and make practical tradeoffs. • Operate what you build: Instrument services with observability tools. • Contribute to technical direction: Help shape architecture and engineering practices. • Effective communication in a dynamic and collaborative environment. • Ownership and impact: Take full ownership of AI solutions developed.

• Build the core backend services: Design, implement, test, and operate the first services for context ingestion, context indexing, retrieval orchestration, API access, source configuration, and system administration. • Create a scalable SaaS foundation: Help define and build the architecture for a multi-tenant service, including tenant isolation, usage tracking, quotas, audit logs, background jobs, and reliable service boundaries. • Power agent-facing retrieval workflows: Build APIs and service interfaces that allow AI agents, MCP tools, CLIs, and internal applications to retrieve relevant context, provenance, confidence signals, and warnings. • Work across product and infrastructure: Partner with the team to make practical tradeoffs between fast experimentation and long-term reliability, especially as the project moves from prototype to production. • Operate what you build: Instrument services with metrics, logs, traces, alerts, and dashboards. Use observability tools to understand system behavior and improve reliability. • Contribute to technical direction: Help shape the architecture, service boundaries, storage choices, API contracts, deployment patterns, and engineering practices for a new product area. • Effective communication: You’ll be working in a highly dynamic and collaborative environment, so we need someone who can communicate effectively and contribute across teams. • Ownership and impact: Take full ownership of the AI solutions you develop, ensuring they are not only innovative but also scalable, maintainable, and aligned with real user workflows.