Staff Forward Deployed Engineer

Join Afresh, a leading AI company focused on revolutionizing the grocery industry. As a Forward Deployed Engineer, you will work closely with enterprise grocery customers, integrating AI systems into their data and building the platform for fast delivery. This role requires a hands-on approach, with dedicated time in the field and on the platform. You will partner with customers, embed with their teams, and design and ship AI-powered systems. Additionally, you will harden what works in the field into the shared platform and build for leverage. This is a senior-level position that offers comprehensive health plans, generous parental leave, equity packages, 401k matching, a flexible vacation policy, and a professional development program.

• Partner with Afresh's account lead and the customer's technical teams to scope and architect the work — the data sources, the architecture, and the path to production.
• Embed with the customer's data and engineering teams (remote and on-site); integrate into their cloud and data platform; build production-grade pipelines and model messy enterprise data into trustworthy data products.
• Harden what works in the field into the shared platform: the knowledge and grounding layer (knowledge graph, ontology, and retrieval) that makes grocery data usable by LLMs, the agent frameworks, and the serving infrastructure.
• Genuine AI/LLM depth — you've built real systems with LLMs and agents (retrieval/RAG, tool-use) and you evaluate quality rather than eyeball it
• A bias toward ownership and momentum, and comfort traveling to customer sites regularly (~10-20%)
• We encourage all highly-qualified candidates to apply, even if they do not fulfill all the listed criteria
• An architect's instinct: you can take an ambiguous problem and a messy data landscape, design a clean and workable solution, and then build it
• Range across both modes — you genuinely like being in front of customers and going heads-down to build reusable infrastructure, and you can switch between them without one suffering. This is the role's defining trait
• Customer-facing comfort: you work well with a customer's engineers and data teams — running working sessions, explaining your thinking, and earning trust through what you deliver
• Real data-engineering depth: building and operating data pipelines, modeling messy enterprise data, and working in a modern cloud data platform (Databricks, BigQuery, Snowflake, or similar)
• 5+ years building production software and data systems, with strong, production-grade code
• Knowledge graphs, ontologies, or semantic layers in production; graph and vector stores (pgvector, Pinecone, Weaviate) and hybrid search
• Experience in grocery, retail, or supply chain data domains
• MCP or similar tool/context protocols; agent frameworks (e.g., LangGraph); MLOps, model serving, and observability for LLM systems
• Prior forward-deployed, solutions, or implementation engineering — or early-stage startup experience navigating rapid customer expansion