Senior AI Platform Engineer

<div class="content-intro"><p>Afresh, the AI platform for grocery, began by tackling the most complex problem in the industry: fresh, and has evolved into the core AI platform for grocers.</p> <p>By leveraging proprietary AI designed for high-volatility environments, we empower partners like Albertsons, Meijer, and Wakefern to drive smarter decisions across their entire enterprise.</p> <p>Following record-breaking 70% revenue growth in 2025, we have scaled to 6 enterprise-grade solutions, with solutions live in over 10% of the U.S. grocery market. Our platform now orchestrates billions of decisions from the store floor to the distribution center and prevented over 200 million pounds of food waste last year alone.</p> <p>If you're looking for a role where your work directly translates into massive scale and social good, and you want to be part of the team that defines how the world eats, there is no better time to join us.</p></div><h3>About the Role</h3> <p>Frontier models are a commodity. The knowledge you feed them is not.</p> <p>As a <strong>Senior AI Platform Engineer</strong>, you build the AI and data platform that powers Afresh's products: the knowledge and retrieval layer that makes grocery data reliably usable by LLMs, the agent systems built on top of it, and the evaluation and serving infrastructure underneath. Your "customers" are Afresh's own engineers and AI products — your job is to give them a platform that turns raw grocery data into context a model can be trusted with, at production quality.</p> <p>This is senior, 0-to-1 platform work. You'll make foundational choices about how we represent grocery knowledge, ground our models, and measure whether any of it is actually working — in a fast-moving space with no playbook.</p> <h3>What You’ll Do</h3> <p><strong>Build the knowledge &amp; retrieval layer</strong></p> <ul> <li>Design and operate the knowledge graph and ontology that capture how grocery data relates.</li> <li>Build the retrieval systems (vector, graph, and structured) that feed the right context to our models — so grounding is reliable, not lucky.</li> </ul> <p><strong>Build and serve the agent platform</strong></p> <ul> <li>Build LLM-powered agents (tool-use, multi-step reasoning, orchestration) and the serving infrastructure to run them reliably and cost-effectively.</li> <li>Build the tools, abstractions, and interfaces other engineers depend on — a platform, not one-off features.</li> </ul> <p><strong>Own evaluation, quality, and the data foundation</strong></p> <ul> <li>Stand up eval sets, LLM-as-judge harnesses, tracing, and observability, plus the metrics (faithfulness, accuracy, hallucinati