About the job
Join Us in Shaping the Future of AI
At Intersnack, we believe that the effectiveness of any AI system hinges on the quality of the knowledge it utilizes. In the pivotal role of AI Data Architect for Knowledge & Governance, you will be tasked with creating a robust and reliable knowledge infrastructure that supports our AI initiatives. This includes designing knowledge graphs, semantic models, vector spaces, and the essential RAG architectures that facilitate seamless agentic workflows.
What We Offer You
This position places you at the core of Intersnack's AI transformation, allowing you to establish a knowledge framework from scratch rather than working with outdated systems. You will wield considerable design authority, shaping ontologies, graph architectures, and governance frameworks that will fundamentally influence AI operations across our global business, spanning over 30 countries. Collaborating with data engineers, AI engineers, and business stakeholders, you will thrive in a dynamic environment where your architectural decisions yield significant, measurable impacts. Our flexible work model allows you to balance time between Düsseldorf and remote work, while the international scope of our program ensures ongoing engagement with complex, large-scale challenges.
Key Responsibilities as Our AI Data Architect - Knowledge & Governance
You will focus on developing the structural intelligence framework that makes enterprise-level AI feasible, crafting knowledge graphs, semantic models, and vector spaces that anchor AI outputs in reliable, well-governed organizational knowledge. In addition to the technical construction, you will spearhead governance and security practices to ensure that every data asset, model, and AI application adheres to Intersnack's standards for transparency, traceability, and compliance with relevant regulations.
Your Responsibilities Include
Crafting and implementing AI-optimized knowledge graphs, semantic models, and vector spaces that serve as the trusted knowledge backbone for analytics and AI functionalities throughout the organization.
Architecting RAG (Retrieval-Augmented Generation) pipelines and frameworks for agent grounding, defining the vision and execution plan.
