Knowledge Management PM
Celonis
| Company | Celonis |
| Category | Product |
| Location | Bangalore |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 10 Jun 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
Celonis is the global leader in Process Intelligence and the pioneer of Process Mining technology. As one of the world’s fastest-growing enterprise SaaS companies, we are changemakers pushing the boundaries of what’s possible. We invest heavily in advanced AI capabilities—specifically our Process Intelligence Graph—to turn data insights into immediate business action. We believe there is a massive opportunity to unlock global productivity and sustainability by placing intelligence at the core of every business process. Join our mission to make processes work for people, companies, and the planet. 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 (KM) PM will serve as the bridge between traditional knowledge architecture and the future of AI-driven enterprise productivity. Supporting the Global Head of KM, you will treat internal knowledge as high-octane fuel for AI models and retrieval systems.
This high-impact role requires a blend of deep technical acumen (Confluence advanced automation, metadata design, and AI/LLM integration) and exceptional interpersonal skills to influence cross-functional teams, lead change management, and act as a trusted advisor across the organization.
The work you’ll do:
1. AI Integration, Prompting & Data Readiness
Support the design and execution of KM strategies that prioritize AI-readiness, ensuring all knowledge assets are structured, clean, and chunked for optimal Retrieval-Augmented Generation (RAG) and search discovery.
Actively develop, test, and refine prompt engineering libraries and context-injection frameworks for internal AI tools to maximize accuracy and minimize model hallucinations.
Partner with the Head of KM to enforce a Global Knowledge Governance Model covering AI ethics, data lineage, privacy, and automated content validation.
Establish robust metadata schemas and taxonomy structures optimized for next-generation vector search and LLM ingestion.
2. Deep Confluence Architecture & Technical Automation
Serve as the resident Confluence Power User and Automation Expert, architecting scalable space hierarchies, page blueprints, and smart workflows using native automation, Smart Values, REST APIs, and third-party integrations (e.g., Jira, Slack/Teams/AI tools).
Build and maintain automated archiving, stale-content flags, and approval workflows to maintain ecosystem hygiene with minimal manual intervention.
Audit and optimize Confluence and connected knowledge repositories to reduce "noise," ensuring AI agents only pull from verified, authoritative sources.
3. Cross-Functional Collaboration & Stakeholder Leadership
Serve as an empathetic and compelling advocate for a "Knowledge-First" culture, building trust across Product, Engineering, Enablement, and Business.
Translate complex AI, data, and technical KM concepts into clear, actionable insights for non-technical leadership and cross-departmental stakeholders.
Partner with L&D and Enablement to design and deliver high-impact training sessions, helping Celonauts effectively interact with AI tools and governed knowledge bases.
4. Agile Project Management & Ecosystem Analytics
Lead end-to-end KM projects using Agile frameworks to keep pace with rapid AI deployment cycles.
Track and analyze detailed platform metrics, search query logs, and user feedback loops to continually optimize AI context windows and knowledge architecture.
Monitor ecosystem health metrics to proactively prevent information decay across all automated knowledge pipelines.
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