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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in Knowledge Management, Content Architecture, and Taxonomy Design, with a focus on optimizing knowledge assets for AI retrieval and performance. Proven ability to structure content for search and maintain high-quality documentation standards.
Highest-signal resume keywords
Knowledge ManagementContent ArchitectureTaxonomy DesignAI Ecosystems FamiliarityDocumentation Development
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Content StructuringKnowledge Asset OptimizationGovernance Standards DefinitionSearch OptimizationRetrieval Workflow DesignDocumentation Patterns DevelopmentQuality AssuranceInstitutional Knowledge CaptureClassification Standards MaintenanceKnowledge Base Management
Soft Skills
Attention to DetailStakeholder PartnershipQuality Orientation
Industry Keywords
Knowledge AssetsAI RetrievalContent StandardsOrganizational ChangeInformation ScienceLibrary ScienceBusiness CommunicationInformation Systems
About the role
Key responsibilities & impact- 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.
- Build & Maintain the AI Knowledge Foundation.
- 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.
- Define Taxonomy, Classification & Content Standards.
- 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.
- Optimize Knowledge for Retrieval & AI Enablement.
- 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.
Requirements
What you’ll need- 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.
- Candidates should demonstrate strong ability to structure content for search, retrieval, or AI-supported workflows, along with excellent attention to detail and strong quality orientation.
- Experience capturing institutional knowledge, developing reusable documentation, and partnering with business stakeholders is important.
- Familiarity with AI ecosystems and the role knowledge plays in enabling skills, connectors, agents, and applications is strongly preferred.
Benefits
Comp & perks- Competitive salary
- Comprehensive range of benefits