Knowledge Management Pm

Celonis Celonis · Data AI · Bangalore, India · Corporate

This role focuses on managing and optimizing internal knowledge assets to fuel enterprise AI productivity, specifically for Large Language Models (LLMs). The Knowledge Management PM will design and implement AI-ready KM strategies, apply prompt engineering, manage global governance (including AI ethics and data privacy), standardize metadata and tagging for AI search, and optimize repository platforms for AI model training. The goal is to ensure high-quality knowledge fuels AI discovery and drives business action.

What you'd actually do

  1. Support the design and implementation of AI-ready KM strategies, ensuring knowledge is captured and formatted for optimized discovery.
  2. Apply advanced prompt engineering techniques to internal AI tools to improve the relevance and accuracy of automated knowledge retrieval.
  3. Assist the Head of KM in implementing a Global Knowledge Governance Model addressing AI ethics, data privacy, and the validation of AI-generated content.
  4. Execute standardized processes for knowledge tagging and storage, ensuring metadata schemas support advanced AI search capabilities.
  5. Manage end-to-end KM projects within the fast-paced timelines required by active corporate AI rollouts.

Skills

Required

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

What the JD emphasized

  • great AI relies entirely on solid KM principles
  • AI-ready KM strategies
  • Prompt Engineering
  • AI Governance
  • AI ethics
  • data privacy
  • validation of AI-generated content
  • advanced AI search capabilities
  • Knowledge First" approach to AI
  • active corporate AI rollouts
  • AI models train on high-quality, current data
  • heavy emphasis on Knowledge Optimisation
  • how KM principles (taxonomies, and metadata) power AI discovery
  • craft and refine prompts to extract high-quality, structured information from AI models
  • risks associated with LLMs

Other signals

  • AI-driven productivity
  • Process Intelligence Graph
  • Knowledge Management
  • LLMs
  • Prompt Engineering
  • AI Governance