Applied AI Engineer

MaintainX is the world's leading AI-powered maintenance and asset management platform, serving 14,000+ customers including Duracell, Shell, Cintas, and Brenntag. We raised $150M in Series D funding led by Bessemer Venture Partners and Bain Capital Ventures, bringing our total funding to $254M. We were named to the Forbes 2025 Cloud 100, the definitive ranking of the top 100 private cloud companies in the world. We're growing fast and hiring the talent to match. 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 What’s In It For You Competitive salary and meaningful equity opportunities. Healthcare, dental, and vision coverage. 401(k) / RRSP enrollment program. Take what you need PTO. A Work Culture where: You’ll work alongside folks across the globe that reflect the MaintainX values: Smart Humble Optimists. We believe in meritocracy, where ideas and effort are publicly celebrated. Our mission is to deliver one platform for maintenance, repair & operations teams to keep the physical world running. We believe the greatest asset in any organization is the people. That’s why we built an intuitive, mobile-first solution to help boost productivity and collaboration across teams and locations. MaintainX is committed to creating a diverse environment. All qualified applicants will receive consideration for employment without regard to race, colour, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

You will own the generation component of the Document Intelligence engine, transforming multimodal data into structured, schema-valid maintenance entities. This involves designing prompt recipes, building evaluation datasets, and ensuring high-quality model output for production services.

Candidates must have strong applied GenAI experience, including prompt engineering, RAG, and structured output implementation. You should have a proven track record of shipping LLM features into production and building robust evaluation rubrics for model performance.

• Competitive salary • Equity opportunities • Healthcare coverage • Dental coverage • Vision coverage • 401(k) enrollment • RRSP enrollment • Paid time off

• Machine learningPreferred
• PythonPreferred

Identified from the job description. Confirm important requirements above.

• GenAI
• Prompt engineering
• RAG
• Multimodal inputs
• LLM evaluation
• Structured output
• Tool-use
• Data engineering
• Python
• OCR
• Machine learning
• Retrieval systems
• Software engineering
• API integration
• Model fine-tuning
• Product impact analysis
• Industrial Repair And Maintenance
• Prompt Engineering
• Generative Artificial Intelligence
• Observability
• Private Cloud
• Artificial Intelligence
• Optical Character Recognition (OCR)
• Asset Management
• Operations
• Trustworthiness

• Technology
• Software
• Engineering
• Data & Analytics
• Manufacturing
• Artificial Intelligence Engineer
• Artificial Intelligence Engineer (General)
• Software Developers

Minimum experience

2+ years

Posting language

English

Working hours

40 hours per week

Location requirements

Country, United States, Canada