AI Data Architect - Knowledge & Governance

The intelligence of any AI system is only as good as the knowledge it is built upon, and this role is responsible for making that knowledge trustworthy, structured, and enduring. As our AI Data Architect for Knowledge & Governance, you will design and build the enterprise-grade knowledge backbone that powers Intersnack's AI programme: knowledge graphs, semantic models, vector spaces, and the RAG architectures that connect them to agentic workflows. You will report into the AI Programme and act as a key voice in shaping not just the technical architecture, but the governance and security frameworks that ensure AI is deployed safely, explainably, and in full compliance with applicable regulation. This is a role for someone who wants to build something that lasts: a change-resilient knowledge infrastructure that scales across procurement, manufacturing, sales, and beyond.

This role sits at the architectural heart of Intersnack's AI transformation, giving you the rare opportunity to define the knowledge foundation from the ground up rather than inherit a legacy system. You will have significant design authority, shaping ontologies, graph architectures, and governance frameworks that will influence how AI operates across a business present in more than 30 countries. Working alongside data engineers, AI engineers, and business stakeholders, you will contribute to a collaborative environment where architecture decisions have visible, measurable impact. Flexibility between Dusseldorf and remote working supports a working model that suits your needs, while the international scope of the programme ensures continuous exposure to complex, real-world challenges at scale.

You will spend your time designing the structural intelligence layer that makes enterprise AI possible: building knowledge graphs, semantic models, and vector spaces that ground AI outputs in trusted, governed organisational knowledge. Alongside the technical build, you will lead the governance and security-by-design practices that ensure every data asset, model, and AI use case meets Intersnack's standards for explainability, traceability, and regulatory compliance.

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  Design and build AI-ready knowledge graphs, semantic models, and vector spaces that form the trusted knowledge backbone for analytics and agentic AI across the enterprise

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  Architect RAG (Retrieval-Augmented Generation) pipelines and agent grounding frameworks, defining the design vision for how agentic AI systems access and reason over organisational knowledge

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  Develop and maintain data governance frameworks, including access management policies, metadata generation, data classification standards, and ethical-by-design principles aligned to Intersnack's security and sovereignty requirements

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  Embed AI TRiSM (AI Trust, Risk, and Security Management) principles into architecture and governance design, ensuring explainability, auditability, and compliant AI usage across all use cases

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  Define and implement ontology models and entity-relationship structures that enable consistent, context-aware knowledge retrieval across structured and unstructured data sources

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  Ensure data lineage, observability, and traceability across the knowledge architecture, enabling stakeholders to trace AI-generated outputs back to their source data

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  Drive change resilience into the data architecture, designing systems that adapt to evolving business requirements, data sources, and AI model updates without structural rework

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  Collaborate with AI engineers, data engineers, and security teams to ensure knowledge infrastructure is secure by design, with protections against prompt injection, data leakage, and model exploitation

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  Coach and enable colleagues on data governance, knowledge architecture principles, and responsible AI usage, contributing to the broader AI literacy programme across Intersnack