Knowledge Management PM

The Team:

The Knowledge Management (KM) team is a global function dedicated to building a robust internal knowledge ecosystem. The team treats internal knowledge assets as the core fuel for enterprise AI productivity, bridging the gap between traditional knowledge architecture and automated discovery to drive high-impact efficiency across the organization.

The Role:

The Knowledge Management PM / Analyst acts as a critical bridge between traditional knowledge management and the future of AI-driven productivity. Supporting the Global Head of KM, you will manage end-to-end projects, engage cross-functional stakeholders, and ensure company knowledge assets are structured, governed, and optimized for Large Language Models (LLMs). This is a high-impact role designed for an expert who understands that great AI relies entirely on solid KM principles.

The work you’ll do:

• KM Strategy & AI Integration: Support the design and implementation of AI-ready KM strategies, ensuring knowledge is captured and formatted for optimized discovery.
• Prompt Engineering: Apply advanced prompt engineering techniques to internal AI tools to improve the relevance and accuracy of automated knowledge retrieval.
• Global Governance: Assist the Head of KM in implementing a Global Knowledge Governance Model addressing AI ethics, data privacy, and the validation of AI-generated content.
• Metadata & Tagging Management: Execute standardized processes for knowledge tagging and storage, ensuring metadata schemas support advanced AI search capabilities.
• Cross-Functional Collaboration: Represent the KM function across departments, advocating for a “Knowledge First” approach to AI and partnering with Enablement and L&D to train the workforce.
• Project Management: Manage end-to-end KM projects within the fast-paced timelines required by active corporate AI rollouts.
• Repository Optimization: Audit and optimize repository platforms like Confluence to remove “noise” and ensure AI models train on high-quality, current data.
• Performance Monitoring: Analyze usage metrics, user feedback, and ecosystem health to prevent information decay and refine prompt libraries.

The qualifications you need:

• Experience: Minimum of 4–6 years of professional experience in Knowledge Management, Operations, or Data Analysis, with a heavy emphasis on Knowledge Optimisation.
• AI Expertise: Hands-on experience with AI tools and a deep understanding of how KM principles (taxonomies, and metadata) power AI discovery.
• Prompt Engineering: Proven ability to craft and refine prompts to extract high-quality, structured information from AI models.
• Governance Knowledge: Strong understanding of AI Governance, including data lineage, accuracy validation, and the risks associated with LLMs.
• Project Leadership: Proven track record of managing multiple complex projects simultaneously in a fast-paced, high-growth environment.
• Stakeholder Management: Exceptional communication skills with the ability to influence senior leaders on the strategic importance of KM in the AI era.
• Analytical Mindset: Proficiency in data analysis and reporting to drive continuous improvement of knowledge assets.