Prompt Engineer / AI Engineer

We're building the kind of platform we always wanted to use : fast, flexible, and built for making sense of real-world complexity. Behind the scenes is a robust, event-driven architecture that connects systems, abstracts messy workflows, and leaves room for smart automation. The surface is clean and simple. The interactions are seamless and intuitive. The machinery underneath is anything but. That’s where you come in.

We’re a well-networked founding team with strong execution roots and a clear roadmap. We’re backed, focused, and delivering fast.

We're looking for a Prompt Engineer / AI Engineer to join early. Someone who knows how to move from prototype to production, who can design prompts, evaluate them, and wrap them in real workflows that run reliably. You’ll work closely with the CTO and Tech Lead to build intelligent systems that plug into a larger product — not just toy demos. If you’re fluent in RAG, LangChain, and PySpark, and care about real-world agent behavior, this is your kind of role.

💥 What You’ll Do

• Build agentic LLM pipelines using LangChain, LangGraph, and LangSmith
• Design and iterate on prompt strategies, with a focus on consistency and context
• Construct retrieval-augmented generation (RAG) systems from scratch
• Own orchestration of PySpark and Databricks workflows to prepare inputs and track outputs
• Instrument evaluation metrics and telemetry to guide prompt evolution
• Work alongside product, frontend, and backend engineers to tightly integrate AI into user-facing flows
• Python (primary language for all LLM + orchestration work)
• LangChain + LangGraph + LangSmith
• Databricks + PySpark for processing, labeling, and training context
• Postgres, and custom orchestration via MCP
• GitHub Actions, GCP

There’s enough here to move fast, but still plenty of room for your fingerprints.

chaos : fixed cycles & cooldowns protect focus and keep context switching low

✅ What We’re Looking For

Experience with PySpark and Databricks to handle real-world data scale

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