AI Knowledge Engineer

CSI•Paducah, KY

About The Position

The AI Knowledge Engineer plays a foundational role in enabling scalable AI capabilities by structuring, organizing, governing, and maintaining the knowledge assets that power AI retrieval and downstream applications. This role ensures institutional knowledge is preserved, optimized, and accessible in ways that improve the performance, consistency, and reliability of AI-driven outputs across skills, connectors, agents, and applications.

Requirements

Bachelor's degree in Knowledge Management, Information Science, Library Science, Communications, Business, Information Systems, or a related field, or equivalent work experience.
5–8 years of progressive experience in knowledge management, content architecture, documentation, taxonomy design, or related disciplines.
Strong ability to structure content for search, retrieval, or AI-supported workflows.
Excellent attention to detail and strong quality orientation.
Experience capturing institutional knowledge, developing reusable documentation, and partnering with business stakeholders.
Familiarity with AI ecosystems and the role knowledge plays in enabling skills, connectors, agents, and applications is strongly preferred.
Applicants must be authorized to work in the United States without the need for sponsorship now or in the future.

Responsibilities

Create and sustain a centralized, reliable, and reusable knowledge base that supports enterprise AI capabilities.
Audit and consolidate knowledge assets across the organization to improve accessibility and reduce fragmentation.
Capture institutional and tribal knowledge, especially during transition, integration, or organizational change.
Structure content so it can be effectively consumed by AI-powered experiences and retrieval workflows.
Establish governance and content architecture standards that improve consistency, searchability, and long-term maintainability.
Define and maintain common taxonomy and classification standards.
Develop scalable documentation patterns and reusable knowledge artifacts.
Ensure content is clean, structured, and aligned to organizational knowledge standards.
Ensure knowledge assets are prepared and maintained in a way that improves retrieval quality and downstream AI performance.
Optimize content for search, retrieval, and AI use cases.
Partner with stakeholders and champions to keep knowledge current, relevant, and aligned to deployed use cases.
Continuously improve knowledge quality, accessibility, and governance processes.