Cologne Bonn Region
Building AI systems that run in production. Not just slides about them. At Apleona, I work in the central AI team responsible for the group's AI strategy and all central AI use cases. We build them on one group-wide enterprise multi-agent AI platform we developed in-house, instead of shipping one-off solutions. What I enjoy most is creating real products. That means covering the whole range from finding use cases and making the business case to strategic and architecture decisions, hands-on development, rolling things out, rethinking everything and helping train people along the way. The hard part of enterprise AI is rarely the model. It's access control, context engineering, communication, data debt, production infrastructure and governance. I like working exactly there. Background: B.Sc. in Business Informatics (Data Science & Big Data), including a semester at UCLA focused on AI & product management.
Part of Apleona's central AI team, responsible for the group's AI strategy and all central AI use cases. Hands-on across strategy, architecture, development, and rollout. -Group AI strategy: prioritizing use cases by business impact and feasibility, with a business case behind every initiative -AI use cases end to end: from use case discovery and business case to production rollout -Group-wide enterprise multi-agent AI platform, developed in-house: chat, shared workspaces, multi-agent orchestration -AI agents rolled out to business units across the group, built on the platform instead of as one-off solutions -Trainings and enablement for employees working with AI -TechStack: Next.js, Azure AI Foundry Agent Service, Azure AI Search (hybrid + semantic reranking), Cosmos DB, Azure Container Apps, Terraform
-AI-Driven Process Optimization: Successfully reduced MVP development by leveraging advanced AI tools. -Prompt Engineering: Designed and fine-tuned prompt-based solutions to enhance the performance and accuracy of AI-driven models. -Retrieval-Augmented Generation (RAG): Contributed to the implementation of advanced RAG systems, enabling efficient information retrieval and integration for business applications. -Requirements Engineering: Conducted comprehensive analysis and documentation of complex business requirements to drive project success. -Change Management: Played a key role in initiatives that fostered the adoption of innovative technologies, improving user engagement and process outcomes. -Cross-Industry Collaboration: Supported the design and deployment of AI-powered processes tailored to diverse industries, ensuring scalable and impactful results.