Vacancies Knowledge Manager AI and Automation

Job Purpose

The Knowledge Manager will lead the organizations transition to an AI-ready knowledge state by establishing strong knowledge governance improving content quality and structure and reducing fragmentation of information spread across multiple tools (e.g. SharePoint ServiceNow Confluence Google Drive Slack/Teams ticketing systems wikis). This role will define and drive a scalable knowledge operating model so content is findable trustworthy reusable and ready for AI-powered search assistants and automation.

Key Activities

1) Knowledge Strategy & AI-Readiness

• Define and deliver a knowledge strategy aligned to business goals and AI enablement (search copilots RAG/LLM assistants automation).
• Establish an AI-ready content framework: standards for structure metadata taxonomy freshness and ownership.
• Partner with data/AI teams to ensure knowledge content can be safely and effectively used in AI applications (e.g. retrieval quality chunking readiness access controls).

2) Content Governance & Operating Model

• Create governance for distributed content teams: ownership approval workflows review cadences and lifecycle management.
• Set policies for what should be documented where it should live and how it should be maintained.

• Define and operationalize KPIs (e.g. findability reuse deflection freshness quality score duplication rate).

3) Tool & Content Consolidation (Multi-Tool Environment)

• Assess current knowledge ecosystem and map content across repositories and workflows.
• Rationalize tools and define source of truth principles and publishing models (authoring vs. consumption layers).
• Lead initiatives to reduce duplication consolidate critical knowledge and improve discoverability across systems.

4) Taxonomy Metadata and Information Architecture

• Design and maintain enterprise taxonomy and controlled vocabulary aligned to products processes customers and internal functions.
• Define required metadata and templates to support filtering search relevance and AI retrieval accuracy.
• Implement content standards: naming conventions tagging rules page structures and content types (FAQs SOPs how-tos troubleshooting policies).

5) Knowledge Quality Lifecycle and Content Excellence

• Establish a content quality framework (accuracy clarity completeness accessibility compliance).
• Run regular audits to identify stale/duplicate/low-value content and drive remediation.
• Create templates style guides and playbooks for authors across the organization.

6) Change Management & Enablement

• Drive adoption of knowledge practices through training communications and stakeholder engagement.
• Build a community of practice for knowledge owners and contributors.
• Coach teams on writing for reuse and AI (task-based writing modular content consistent terminology).

7) Cross-Functional Collaboration

• Partner with IT Security Legal/Compliance Product Support/Customer Success HR and Operations to ensure knowledge is governed and usable.
• Align knowledge practices with enterprise search identity/access management and data privacy requirements.
• Support AI initiatives by providing curated high-quality knowledge sources and feedback loops.

Experience

Required Skills & Experience

• 510 years in knowledge management content operations information architecture or enterprise content management.
• Experience building governance models and driving adoption across multiple teams/tools.
• Strong understanding of taxonomy metadata and content lifecycle management.
• Practical experience improving enterprise search and/or preparing content for AI retrieval (RAG) copilots or automation.
• Excellent stakeholder management and change leadership skills.
• Strong writing/editing and content design skills (clarity reuse structured content).
• Experience with tools such as Confluence SharePoint ServiceNow Knowledge Zendesk Guide Salesforce Knowledge Notion Guru or similar.
• Familiarity with search technologies (Microsoft Search Elastic Coveo Google Cloud Search) and relevance tuning.
• Understanding of AI/LLM concepts (RAG embeddings chunking prompt patterns evaluation of answer quality).
• Experience in regulated environments with content compliance retention and access controls.