Joblaze summary

In the role of Applied AI Engineer at MaintainX, the individual will focus on transforming multimodal data into structured maintenance knowledge, ensuring quality in the generation process. Key skills include expertise in prompt engineering, evaluation dataset creation, and familiarity with multimodal inputs like video and audio. This position is ideal for someone with a strong background in applied GenAI and experience in deploying LLM features in production environments. The team values a culture of meritocracy and collaboration, reflecting the company's mission to enhance operational efficiency.

Joblaze insights

Listed about a month ago — first seen on Joblaze September 3, 2026. Last confirmed on MaintainX's careers page October 27, 2026.
This exact title is also open at 1 other location at MaintainX: San Francisco.
GenAI appears in 0.7% of 822 comparable mid ai/ml roles; Document Intelligence appears in 0.1% of 822 comparable mid ai/ml roles.

Quick facts

What's the tech stack?: Joblaze extracted these technologies from the posting: Document Intelligence, GenAI, LLMX, OCR.
What seniority level is this role?: MaintainX targets mid-level candidates for this position.
Is this full-time or contract?: Full-time for this Applied AI Engineer role at MaintainX.

From the original posting

Frontier models are getting commoditized. The operating data underneath them isn't — and that's what we own. 13.9M+ managed assets, 79.5M+ completed work orders, and 150,000+ technicians generating trustworthy operating data every week, at the point of work.

Document Intelligence is the horizontal engine that turns that raw material — any file a customer hands us — into structured, trustworthy maintenance knowledge, and into the AI-native entities and answers built on top of it.

The Role

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.

Minimum Requirements:

Strong applied GenAI craft - prompt engineering, structured output / tool-use, RAG and retrieval-context patterns
Real eval discipline: you've built datasets and rubrics, measured factuality/relevance/quality, and closed the loop on regressions yourself
Shipped LLM features into production services, not notebooks, and can connect model performance to product impact
Comfort working with multimodal inputs - video, PDF, audio, image - converted to text or structured output

Nice to have:

LLM observability / cost awareness, or experience with an eval platform
Document, PDF, or video understanding; OCR; retrieval systems
Light fine-tuning experience, or familiarity with the industrial/maintenance domain

Standard company text repeated across MaintainX's postings is omitted here.

Applied AI Engineer

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