Own the technical direction and end-to-end delivery of a backend engineering team supporting high-volume supply chain visibility platforms. Design distributed microservices, event-driven systems, data models, integrations, and production architecture. Drive engineering standards, reliability, observability, technical debt reduction, risk management, code quality, mentoring, hiring, and cross-functional coordination. Remain hands-on in Java or Go development while partnering with Product and Engineering Managers to deliver complex multi-quarter initiatives.

Own the reference architecture and platform for production AI agents, including agent execution, tool use, state management, recovery, context engineering, model routing, guardrails, and human handoffs. Build shared retrieval, memory, and evaluation infrastructure for offline and online quality measurement. Integrate agents with event-driven systems and customer APIs, review designs, mentor engineers, and connect technical metrics to supply chain business outcomes.

Build, deploy, monitor, and retrain machine learning models for ETA forecasting, status extraction, and logistics automation. Develop NLP and LLM pipelines, manage end-to-end production ML workflows, diagnose performance gaps, and translate model improvements into business value. Collaborate with product, engineering, and operations teams while mentoring data scientists and making architecture tradeoffs.