Position: AI Knowledge Management Lead
The AI Knowledge Management Lead will make Northleaf's trusted content and institutional knowledge usable for AI-enabled work. As a member of Northleaf's AI Platform team, you will own the business content architecture and AI-readiness framework for Northleaf's governed knowledge estate, including priority content domains, taxonomy, metadata, source-of-truth rules, content ownership, access expectations, refresh standards and business-area onboarding.
Northleaf is pursuing a business-led, platform-enabled AI model. This is a senior business-technical knowledge leadership role, where you will define content standards, readiness criteria and business validation of retrieval quality while partnering with M365, SharePoint, AI/search, data, privacy, security and records specialists on technical implementation and controls.
Key Responsibilities
- Establish Northleaf's AI-ready knowledge and content architecture, including content domains, SharePoint site/library patterns, knowledge bases, taxonomy, metadata, document-readiness standards, source-of-truth rules, content ownership and refresh cadence.
- Partner with business teams to identify, prepare and onboard priority content into protected AI-ready knowledge environments for search, agents and repeatable knowledge use.
- Coordinate content tagging, clean-up, migration readiness and onboarding activities with business owners and platform teams so trusted document sets can support repeatable briefs, searches, reports and knowledge workflows across approved content.
- Translate business workflows into AI-ready content and retrieval requirements, including source authority, source-of-truth and derivative-content rules, access expectations, sensitivity considerations, metadata requirements, citations, human review points and exception handling.
- Partner with M365, SharePoint, Purview, AI/search, data and automation specialists on implementation patterns involving SharePoint Premium/Syntex, Microsoft 365 Copilot, Microsoft Foundry, Azure AI Document Intelligence, Azure AI Search, Blob Storage and related components as required.
- Define business test cases and acceptance criteria to evaluate retrieval quality, search relevance, answer grounding, citation usefulness, source traceability and permissions safety; use feedback and quality measures to refine taxonomy and metadata.
- Partner with AI data product, agent and automation leads to ensure document-based workflows, extracted-data outputs and reusable AI patterns are grounded in approved, well-structured content.
- Support agents, document-processing flows and scheduled synthesis/reporting by defining the content requirements, source rules and quality checks needed for reusable workflow patterns.
- Create repeatable playbooks, onboarding materials, ownership practices and office-hours support so business teams can progressively adopt content and knowledge standards without relying on one-off central support.
- Track content-readiness progress, business-team onboarding, quality issues, backlog priorities, ownership gaps and foundation delivery milestones; communicate practical risks, dependencies and recommendations to leadership and partner teams.
Qualifications
- 12+ years of experience across knowledge management, information architecture, enterprise content management, enterprise search, digital workplace, document governance or related transformation roles.
- Demonstrated experience designing and implementing taxonomies, metadata models, document ownership structures, content lifecycle processes, access/permission models, content standards and business stewardship practices.
- Strong working knowledge of SharePoint content architecture and governance, including sites, libraries, permissions, metadata and content lifecycle; familiarity with Teams, Microsoft Search, Copilot, Purview, Power Platform, SharePoint Premium/Syntex or comparable knowledge and content-management capabilities is an asset.
- Practical AI retrieval literacy, including how source quality, document structure, metadata, permissions, OCR/extraction, chunking, search relevance, grounding and citations affect AI-generated outputs.
- Ability to partner credibly with platform and engineering specialists on Azure/Microsoft capabilities such as Microsoft Foundry, Azure AI Document Intelligence, Azure AI Search, Blob Storage, data connectors, document-processing pipelines and agent workflows. Hands-on AI or cloud engineering is not required, but the ability to define requirements and evaluate output quality is essential.
- Experience working in a regulated, confidential and document-intensive environment; private markets, asset management, banking, pension, insurance, legal, professional services or consulting experience is strongly preferred.
- Strong business-facing stakeholder management skills, with the ability to translate technical and information-management concepts into practical workflows for investment and corporate teams.
- Excellent written and verbal communication skills, strong judgment, attention to detail and the ability to influence without direct authority in a collaborative, high-integrity environment.
- University degree or equivalent experience in information management, library and information science, business, technology, data, knowledge management or a related field.
Location
Toronto, Canada
