Applied AI Engineer

You'll own the Generation half of Document Intelligence: turning multimodal primitives (keyframes, transcripts, OCR) into schema-valid entities like SOPs, and holding the line on quality so "fast" never becomes "fast and wrong."

Design and iterate recipe prompts — system, few-shot, and context assembly — for each generation recipe

Define per-entity target schemas and domain validators (procedure step-type rules, field caps) that generated output has to pass

Build generation-quality eval datasets and rubrics, offline and online, running on LLMX's eval pipeline, and close the loop when quality regresses

Assemble multimodal context windows from keyframes, transcript, and OCR so each model call has exactly what it needs

Choose the model and token budget per recipe based on quality, cost, and latency tradeoffs

You'll ride on LLMX for model access and on Attachments for ingestion, and hand off validated entities to the domains that own them. You'll report to our Engineering Lead and work closely with the Processing side of Document Intelligence.

LLM observability / cost awareness, or experience with an eval platform

Document, PDF, or video understanding; OCR; retrieval systems