{"schemaVersion":"jobsearcher.job.v1","id":"fbb44ac19f1f8eaa76f409d0","url":"https://jobsearcher.com/jobs/fbb44ac19f1f8eaa76f409d0","canonicalUrl":"https://jobsearcher.com/jobs/fbb44ac19f1f8eaa76f409d0","title":"Senior Forward Deployed Engineer","description":"Responsibilities\n\nMost companies make you choose: build the platform, or go deploy it. Here you do both — and that’s the point\n\nAs a Forward Deployed AI Engineer, you’re part of a single team that both delivers Afresh’s AI into enterprise grocery customers and builds the platform that makes that delivery fast\n\nYou’ll spend dedicated time in the field — embedded with a customer, integrating into their data, shipping AI systems on top of it — and dedicated time on the platform, turning what you just learned into reusable tooling the whole team deploys next\n\nYou build the house you live in\n\nAfresh leads the customer relationship and direction; you and a small team bring the technical firepower — scope and architect the work with the customer, then build it\n\nBecause you also own the platform underneath, the rough edges you hit in the field become the things you fix at the root\n\nThis is senior, hands‑on, 0‑to‑1 work in a space with no playbook\n\nIn the field (forward deployed):\n\nPartner 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\n\nEmbed 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\n\nDesign and ship LLM‑ and agent‑powered systems on that data — retrieval, agentic workflows, data‑quality and analytics agents — reliable enough to run in production, not just to demo\n\nOn the platform (building the house you live in):\n\nHarden 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\n\nBuild the evals, tracing, and tooling that let the team measure quality — accuracy, hallucination rate, latency, cost — and ship faster on the next customer\n\nBuild for leverage: clean interfaces and reusable building blocks, not one‑off per‑customer code\n\nAcross both:\n\nOwn the flywheel: field learnings flow straight into the platform, and platform improvements show up at the next customer\n\nBenefits\n\nComprehensive health plans\n\nGenerous parental leave\n\nEquity packages\n\n401k matching\n\nFlexible vacation policy\n\nProfessional development program\n\nWork from home stipend\n\nQualifications\n\nReal 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)\n\nAn architect’s instinct: you can take an ambiguous problem and a messy data landscape, design a clean and workable solution, and then build it\n\nGenuine AI/LLM depth — you’ve built real systems with LLMs and agents (retrieval/RAG, tool‑use) and you evaluate quality rather than eyeball it\n\nRange 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\n\nCustomer‑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\n\nWe encourage all highly‑qualified candidates to apply, even if they do not fulfill all the listed criteria\n\n3+ years building production software and data systems, with strong, production‑grade code\n\nA bias toward ownership and momentum, and comfort traveling to customer sites regularly (~10‑20%)\n\nExperience in grocery, retail, or supply chain data domains\n\nKnowledge graphs, ontologies, or semantic layers in production; graph and vector stores (pgvector, Pinecone, Weaviate) and hybrid search\n\nMCP or similar tool/context protocols; agent frameworks (e.g., LangGraph); MLOps, model serving, and observability for LLM systems\n\nPrior forward‑deployed, solutions, or implementation engineering — or early‑stage startup experience navigating rapid customer expansion\n\n#J-18808-Ljbffr","company":"Afresh Technologies","rawCompany":"afresh technologies","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-07-19T03:14:27.969Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior Forward Deployed Engineer","description":"Responsibilities\n\nMost companies make you choose: build the platform, or go deploy it. Here you do both — and that’s the point\n\nAs a Forward Deployed AI Engineer, you’re part of a single team that both delivers Afresh’s AI into enterprise grocery customers and builds the platform that makes that delivery fast\n\nYou’ll spend dedicated time in the field — embedded with a customer, integrating into their data, shipping AI systems on top of it — and dedicated time on the platform, turning what you just learned into reusable tooling the whole team deploys next\n\nYou build the house you live in\n\nAfresh leads the customer relationship and direction; you and a small team bring the technical firepower — scope and architect the work with the customer, then build it\n\nBecause you also own the platform underneath, the rough edges you hit in the field become the things you fix at the root\n\nThis is senior, hands‑on, 0‑to‑1 work in a space with no playbook\n\nIn the field (forward deployed):\n\nPartner 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\n\nEmbed 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\n\nDesign and ship LLM‑ and agent‑powered systems on that data — retrieval, agentic workflows, data‑quality and analytics agents — reliable enough to run in production, not just to demo\n\nOn the platform (building the house you live in):\n\nHarden 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\n\nBuild the evals, tracing, and tooling that let the team measure quality — accuracy, hallucination rate, latency, cost — and ship faster on the next customer\n\nBuild for leverage: clean interfaces and reusable building blocks, not one‑off per‑customer code\n\nAcross both:\n\nOwn the flywheel: field learnings flow straight into the platform, and platform improvements show up at the next customer\n\nBenefits\n\nComprehensive health plans\n\nGenerous parental leave\n\nEquity packages\n\n401k matching\n\nFlexible vacation policy\n\nProfessional development program\n\nWork from home stipend\n\nQualifications\n\nReal 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)\n\nAn architect’s instinct: you can take an ambiguous problem and a messy data landscape, design a clean and workable solution, and then build it\n\nGenuine AI/LLM depth — you’ve built real systems with LLMs and agents (retrieval/RAG, tool‑use) and you evaluate quality rather than eyeball it\n\nRange 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\n\nCustomer‑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\n\nWe encourage all highly‑qualified candidates to apply, even if they do not fulfill all the listed criteria\n\n3+ years building production software and data systems, with strong, production‑grade code\n\nA bias toward ownership and momentum, and comfort traveling to customer sites regularly (~10‑20%)\n\nExperience in grocery, retail, or supply chain data domains\n\nKnowledge graphs, ontologies, or semantic layers in production; graph and vector stores (pgvector, Pinecone, Weaviate) and hybrid search\n\nMCP or similar tool/context protocols; agent frameworks (e.g., LangGraph); MLOps, model serving, and observability for LLM systems\n\nPrior forward‑deployed, solutions, or implementation engineering — or early‑stage startup experience navigating rapid customer expansion\n\n#J-18808-Ljbffr","datePosted":"2026-07-19T03:14:27.969Z","dateModified":"2026-07-19T03:14:27.969Z","hiringOrganization":{"@type":"Organization","name":"Afresh Technologies","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"fbb44ac19f1f8eaa76f409d0"},"url":"https://jobsearcher.com/jobs/fbb44ac19f1f8eaa76f409d0"}}