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Metadata Tagging Jobs in Ontario (NOW HIRING)

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 ...

Assisting with metadata tagging, file versioning, and content tracking as part of maintaining a clean, sustainable, and easy-to-manage learning content library. * Participating in creative ...

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Experience defining enterprise knowledge management, taxonomy, metadata tagging, and content governance strategies that enable trusted AI systems. * Experience with enterprise knowledge platforms ...

Maintain and update metadata in the business glossary and data catalog. * Work with business ... Promote consistent tagging, access controls, and best practices to improve discoverability and ...

... tagging), and end-to-end traceability across the toolchain * Drive Data Hub adoption metrics ... Build and maintain a Data Product catalog with discoverable metadata, lineage, quality scores, and ...

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Metadata Tagging information

What are the key skills and qualifications needed to thrive as a Metadata Tagging Specialist, and why are they important?

To thrive as a Metadata Tagging Specialist, you need a solid understanding of information organization, taxonomy standards, and attention to detail, often supported by a degree in library science, information management, or a related field. Familiarity with metadata management tools, content management systems (CMS), and standards like Dublin Core or schema.org is typically required. Strong analytical thinking, organizational skills, and the ability to communicate clearly with content creators make someone stand out in this role. These skills ensure that digital assets are accurately categorized and easily retrievable, enhancing discoverability and workflow efficiency.

What is metadata tagging?

Metadata tagging is the process of assigning descriptive information, or 'tags,' to digital content to make it easier to organize, search, and retrieve. These tags can include keywords, categories, dates, authors, and other relevant data points. Accurate metadata tagging improves the discoverability of content in databases, websites, and digital libraries, supporting better information management. It's a crucial task for industries dealing with large volumes of digital assets, such as media, publishing, and e-commerce.

What are some common challenges faced by professionals in metadata tagging, and how can they be addressed?

One of the main challenges in metadata tagging is ensuring consistency and accuracy across large volumes of digital assets, especially when multiple team members are involved. Discrepancies can lead to difficulties in asset retrieval and negatively impact workflows. Adopting standardized metadata schemas, using controlled vocabularies, and regularly reviewing tagging practices can help mitigate these issues. Collaboration with content creators and IT teams is also essential to align tagging strategies with organizational goals and technical requirements.
Infographic showing various Metadata Tagging job openings in Ontario as of July 2026, with employment types broken down into 90% Full Time, 4% Part Time, 1% Temporary, 4% Contract, and 1% Nights. Highlights an 83% Physical, 6% Hybrid, and 11% Remote job distribution.

AI Knowledge Operations Specialist

Zafin

Toronto, ON

CA$90K - CA$130K/yr

Other

Posted 9 days ago


Job description

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 gate pass rate.

What Do You Need to Succeed? 

Must Haves 

  • Degree in Comp Sci, Library/Information Science, Business, or related field, or equivalent experience.
  • Experience with content management, metadata tagging, and taxonomy design.
  • Familiarity with markdown, documentation tooling, and repository maintenance.
  • Understanding of how knowledge quality directly affects AI agent performance.
  • Strong research and analytical skills, with the ability to convert unstructured information into AI agent-usable formats.
  • Working understanding of enterprise data architecture and integration patterns as they relate to knowledge and content systems.
  • Familiarity with governance rhythms, including monthly reviews and quarterly maturity assessments, in a regulated environment.

Nice to Have 

  • Familiarity with banking processes and policy frameworks.
  • Experience supporting an audit or regulatory examination involving AI or automated decision-making.

Additional Job Details 

  • Expected Salary Range: $90,000 - $130,000
  • Vacancy Status: Open position(s) to be filled
  • Mode of Work: Hybrid
  • Use of AI: Zafin may use Artificial Intelligence (AI) and/or other forms of automated technology to screen and/or assess applicants for this position. Zafin will not utilize AI for conducting interviews and/or making hiring decisions.