AI Knowledge Operations Specialist
Zafin
| Company | Zafin |
| Category | Uncategorised |
| Location | Toronto |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 23 Jul 2026 |
| Last verified | 2 Aug 2026 |
| Source | Employer ATS (greenhouse) |
Description
Zafin is an AI platform company helping regulated institutions modernize how critical work is designed, governed, and delivered. Our technology enables organizations to move faster while maintaining the governance, accountability, and control required in highly regulated environments.
Our portfolio includes Zafin AIOS , an agent orchestration platform for governed AI work; the Zafin Banking Platform , which helps banks modernize product, pricing, offers, billing, loyalty, and relationship management; and Zafin IO , an integration platform that connects data, systems, and workflows across complex enterprise environments.
Headquartered in Toronto, Canada, Zafin partners with leading financial institutions across North America, Europe, the Middle East, Africa, and Asia-Pacific. As AI transforms the future of financial services, we're building the platforms that help regulated organizations adopt AI responsibly and at scale. What’s the Opportunity?
The AI Knowledge Operations Specialist is responsible for preparing, organizing, governing, and maintaining the knowledge assets that power Zafin’s AI Operating System (AIOS). This role ensures the business knowledge is structured, trusted, current, and optimized for AI agent consumption and enabling accurate and reliable, and explainable AI solutions across client engagements.
Working closely with Industry Consultants, Agent Engineers, AI Evaluation Engineers, and other team members, this role prepares AI-ready content through research, metadata management, taxonomy design, document structuring, and knowledge lifecycle management. The role owns the Knowledge and Governance pillars of the Embed phase: capture, curation, publication, and reuse of knowledge on one side; evaluation, security, auditability, and operating controls on the other. The role applies established governance standards and operational controls to ensure knowledge assets remain consistent, reusable, compliant, and production ready.
The role also supports the execution of AI governance by maintaining knowledge quality, preparing audit evidence, monitoring content health, and contributing to governance reviews and continuous improvement activities. Every engagement should leave behind trusted, reusable knowledge assets that improve AI performance over time.
What Will You Do?
Prepare, clean, structure, and maintain content to support consumption by AI agents.
Perform markdown cleanup, metadata tagging, taxonomy management, and content migration to improve AI agent performance.
Maintain and continuously improve the knowledge repository that underpins AI agent accuracy, reliability, and reuse
Conduct research to extract business knowledge and convert it into AI agent-ready assets and decision frameworks.
Document processes, policies, and procedures to the standard required in a regulated banking environment.
Manage the capture, curation, publication, reuse, archival, and retirement of knowledge assets across client engagements.
Apply established knowledge governance standards to ensure AI content quality, consistency, and traceability.
Validate metadata, taxonomy, document structure, and content quality before production use.
Identify outdated, duplicated, conflicting, or incomplete knowledge and coordinate remediation.
Monitor knowledge quality metrics and recommend improvements that increase AI accuracy and reliability.
Support governance reviews by preparing documentation, audit evidence, and knowledge artifacts.
Contribute knowledge quality data supporting AI quality and risk reporting.
Support investigation of knowledge-related AI failures and contribute to corrective actions.
Participate in the monthly governance review and the quarterly operating model review, reporting on knowledge and AI risk status.
Provide the data underpinning the AI agent quality and risk metrics layer: escaped defect rate, architecture compliance, and security
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