Job Description

Job Description

We’re seeking a proactive and systems-minded Content Manager to oversee the creation, maintenance, and governance of content across our internal AI knowledge center. This knowledge center is a centralized resource for AI-related tools, documentation, best practices, and learning materials, designed to empower teams across our organization. You will collaborate with subject matter experts, product managers, and engineers to ensure our content is clear, consistent, and accessible. This role bridges knowledge management strategy, process design, content quality assurance, and technical writing to ensure our content ecosystem is accurate, consistent, and user-friendly. The ideal candidate enjoys collaborating with cross-functional teams, and has a keen eye for both detail and design. You’ll play a key role in shaping how AI knowledge is documented, distributed, and maintained, guiding contributors, and continuously improving how information is shared across our organization.

Requirements:

• 5+ years of experience in content management, knowledge management, or technical writing for software, data science, or AI/ML projects
• Must be proficient in Confluence, including automations and AI features.
• Experience building or maintaining centralized knowledge systems in technical environments.
• Familiarity with information architecture concepts and structured content methodologies (taxonomy, tagging, metadata, governance).
• Strong attention to detail, editorial judgment, and ability to simplify complex information.
• Excellent communication and collaboration skills.
• Ability to collaborate with technical and non-technical stakeholders.
• Experience with AI/ML concepts or tools is a plus.

Required Experience:

1. Experience with Confluence/Atlassian
2. Tech background, strong familiarity with technical terminology
3. Process/Coaching Experience
4. Ability to partner with engineering, product, and design teams to translate technical concepts into clear, structured documentation
5. Experience defining content standards and governance models

Key Responsibilities

Content Organization:

• Audit, categorize, and structure existing technical documentation and resources within the AI Hub.
• Develop and maintain a logical, user-friendly information architecture.
• Utilize tagging, links, and other macros to help users navigate through content.
• Standardize best practices for tagging, naming conventions, formatting, and content lifecycle management

Collaboration:

• Work closely with AI Hub stakeholders, engineers, and product managers to gather information and validate content.
• Facilitate content review cycles and incorporate feedback from technical and non-technical users.
• Provide frameworks, templates, checklists, style guides, and best practices for contributors.
• Provide hands on support for contributors.

Content Creation and Review:

• Edit contributor content for clarity, formatting, professionalism, and audience. Ensure content adheres to standards.
• Write, edit, and update technical guides, FAQs, onboarding materials, and best practices for AI tools and workflows.
• Translate complex technical concepts into clear, concise, and engaging content for internal audiences.
• Ensure consistency, clarity, and accuracy across AI-related content areas
• Improve the structure, discoverability, and readability of high-impact content used across engineering, design, and product teams

Content Management:

• Manage version control and updates for evolving AI tools and documentation.
• Ensure all content meets internal standards for quality, consistency, and accessibility.

Process Improvement:

• Recommend and implement improvements to content workflows, templates, and publishing processes.
• Identify gaps in documentation and propose solutions to address them.
• Utilize Confluence automations and AI to streamline the contribution and review process.
• Define and continuously refine content governance, workflows, approval processes, and contributor enablement
• Monitor content health and analytics to surface gaps, usage patterns, and improvement opportunities

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