Demonstrated experience shipping stateful, policy-controlled agent runtimes and tool-using multi-agent systems for autonomous long-horizon work: durable session lifecycle, context-window management, retrieval and RAG, structured outputs, guardrails, evaluation systems, secure delegation, safe tool use, interruption handling, and recovery-built on established orchestration frameworks or on custom infrastructure of your own design, including MCP and production tool and integration lifecycle management. 8+ years of hands-on software, machine learning, or applied AI engineering experience with Staff/Principal-level technical impact across teams-capable of independently taking a frontier-grade, full-stack agentic product from prototype to production (agent runtime, CLI/TUI, web frontend, backend services and APIs, and the underlying data layer) while remaining deeply involved in code, design, delivery, and production operations.