Teams at Afresh

Own complex technical support escalations involving data mapping, forecasts, recommendations, integrations, and data feeds. Conduct root-cause analysis, build SQL and Python tooling, create Databricks and Looker dashboards, develop AI-driven workflows, and measure operational impact. Lead cross-functional initiatives with Engineering, Product, Data, Solutions, and Operations teams. Mentor support engineers, document playbooks, present findings to leadership, and drive systemic improvements that reduce ticket volume, handling time, and escalations.

Build and lead Afresh’s forward deployed engineering team, including hiring, execution, architecture, and technical customer relationships. Oversee AI platform integrations across enterprise grocery customers, define reference architectures, establish production standards, and decide which capabilities belong in the shared platform. Partner with platform engineering to convert deployment learnings into repeatable products and playbooks. The role is player-coach, requiring architecture reviews, technical discovery, debugging, customer engagement, and approximately 25–40% travel.

Embed with grocery customers to scope, integrate, and deploy production LLM- and agent-powered systems. Build and harden platform components (knowledge graph/ontology, retrieval, agent frameworks, serving, evals, and observability) so field learnings become reusable tooling. Balance customer-facing implementation with platform engineering to enable scalable, reliable AI in production.