# Staff AI Platform Engineer at Afresh

Afresh is hiring a Staff AI Platform Engineer to build the foundational knowledge and retrieval systems that enable their AI to make smarter decisions across grocery operations. You'll design knowledge graphs and ontologies that translate messy retail data into reliable context for LLMs, build agent platforms and serving infrastructure for production AI, and establish the evaluation frameworks that measure whether these systems actually work. This is 0-to-1 platform work in a fast-moving space without a established playbook—you're making foundational architectural choices about how Afresh represents grocery knowledge and grounds its models.

You should have 5+ years building production software or ML systems with hands-on experience deploying LLM systems (RAG, agents, evaluation), strong data engineering fundamentals with modern cloud stacks like Databricks and MLflow, and comfort navigating the messy realities of AI systems—latency and cost trade-offs, non-determinism, hallucinations. Platform-minded engineers who build for leverage and clean interfaces will thrive. Nice-to-have experience includes knowledge graphs in production, vector databases, agent frameworks, MLOps at scale, and exposure to complex enterprise data domains like retail.

This is a full-time role based in San Francisco with a company that's scaled to serve over 10% of the U.S. grocery market and prevented 200 million pounds of food waste last year. You'd be working in Python, Databricks, dbt, Airflow, and Claude, with the chance to build systems that directly impact both scale and social good.

Location: San Francisco, CA

Apply: https://getfoundi.com/apply/staff-ai-platform-engineer-afresh-bdf906c6

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