GenAI Engineer — Multimodal Document Intelligence

Jobtailor; California (MO); On-site; USD 140,000 - 180,000; Full time

Job summary

Jobtailor is seeking a Generation Lead for Document Intelligence to design prompts, build context windows, and shape per-entity schemas for reliable AI-generated outputs.

You will own evaluation datasets, monitor quality, and collaborate with the Engineering and Processing teams to deliver production-ready GenAI features. This role emphasizes cost-aware model selection and multimodal understanding.

Qualifications

• Strong GenAI craft with prompt engineering and structured outputs.
• Experience building datasets and rubrics; measure quality.
• Experience shipping LLM features into production.
• Ability to connect model performance to product impact.
• Experience with multimodal inputs and OCR data.
• Familiarity with eval platforms is a plus.

Responsibilities

• Own the Generation half of Document Intelligence.
• Design and iterate system prompts, few-shot prompts, and context assembly for generation recipes.
• Define per-entity target schemas and domain validators.
• Build generation-quality evaluation datasets and rubrics, offline and online, using eval pipeline.
• Monitor and address quality regressions.
• Assemble multimodal context windows from keyframes, transcripts, and OCR.
• Choose models and token budgets based on quality, cost, and latency tradeoffs.
• Use LLMX for model access and Attachments for ingestion.
• Hand off validated entities to domain-owning teams.
• Report to the Engineering Lead and collaborate with the Processing side of Document Intelligence.

Skills

GenAI Craft

Prompt Engineering

Dataset Building

Quality Evaluation

Model Performance

Multimodal Handling

RAG & Retrieval

LLM Features

Tools

LLMX

Eval Pipeline

OCR

Retrieval Systems

Job description

Jobtailor is seeking a Generation Lead for Document Intelligence to design prompts, build context windows, and shape per-entity schemas for reliable AI-generated outputs.

You will own evaluation datasets, monitor quality, and collaborate with the Engineering and Processing teams to deliver production-ready GenAI features. This role emphasizes cost-aware model selection and multimodal understanding.