Staff AI Platform Engineer
About The Position
Frontier models are a commodity. The knowledge you feed them is not. As a Staff AI Platform Engineer, 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. 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.
Requirements
- 5+ years building production software, data, or ML systems; an excellent engineer with strong systems and API design (Python).
- Hands-on production experience with LLM systems: retrieval/RAG, agents and tool-use, prompt and context engineering — and, critically, evaluation. You measure quality; you don't eyeball it.
- Solid data-engineering and data-platform foundations: pipelines, data modeling, and a modern cloud data stack (Databricks/Spark, MLflow, cloud warehouses).
- Comfort in the messy middle of AI systems — retrieval quality, latency and cost trade-offs, non-determinism — and the instinct to build the guardrails and evals that make them trustworthy.
- A platform mindset: you build for leverage and clean interfaces, and you thrive in ambiguity in a fast-moving space.
Nice To Haves
- Knowledge graphs, ontologies, or semantic layers in production; graph databases.
- Vector stores (pgvector, Pinecone, Weaviate, etc.) and hybrid search.
- MCP or similar tool/context protocols; agent frameworks (e.g., LangGraph).
- MLOps and model serving at scale; experimentation and observability tooling for LLM systems.
- Experience in grocery, retail, or other complex enterprise data domains.
Responsibilities
- Build the knowledge & retrieval layer: Design and operate the knowledge graph and ontology that capture how grocery data relates. Build the retrieval systems (vector, graph, and structured) that feed the right context to our models — so grounding is reliable, not lucky.
- Build and serve the agent platform: Build LLM-powered agents (tool-use, multi-step reasoning, orchestration) and the serving infrastructure to run them reliably and cost-effectively. Build the tools, abstractions, and interfaces other engineers depend on — a platform, not one-off features.
- Own evaluation, quality, and the data foundation: Stand up eval sets, LLM-as-judge harnesses, tracing, and observability, plus the metrics (faithfulness, accuracy, hallucination rate, latency, cost) that tell us whether a change helped or hurt. Build the pipelines, data products, and experimentation on Databricks/MLflow that take work from prototype to production — and partner with the engineers deploying our AI in the field to harden what works into reusable capabilities.
Benefits
- Comprehensive medical, dental, and vision coverage for you and your family, with the majority of premiums covered by Afresh.
- Dedicated mental health support and counseling services.
- Competitive base salary
- Meaningful equity (U.S. employees)
- 401(k) program with a generous company match.
- Home office stipend
- "Coworking Wallets" for flexible workspace access.
- Annual professional development budget
- Holistic Monthly Stipends for "Betterment" (wellness/lifestyle) and telecommunications
- Flexible paid time off
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
