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.

Build and productionize AI agents for customer support, voice interactions, and carrier onboarding. Develop primarily backend applications in Python and Java or Go, design databases and APIs, integrate frontend components, troubleshoot production workflows, conduct code reviews, and deploy services with Kubernetes. Collaborate with product, engineering, DevOps, US teams, and customers to deliver supply chain platform capabilities.