I am a Master’s graduate in Cognitive Systems from Ulm University with a strong focus on applied artificial intelligence, data-driven systems, and production-ready machine learning. My background combines hands-on AI engineering, software development, and close collaboration with domain experts to translate complex requirements into reliable, scalable solutions. During my internship with the Data Science team at Liebherr Group, I have worked on multiple AI projects spanning the full lifecycle from data ingestion and preprocessing to model development, evaluation, and deployment preparation. My work includes building pipelines to extracting information from unstructured technical data, developing RAG-based chatbots for efficient document search, and contributing to computer vision solutions for safety-critical environments. Across these projects, I have gained experience building and managing AI-based applications, as well as developing different approaches to designing and operating AI systems in practice. Previously, I worked for three years as a Software Engineer at PwC, where I developed and maintained production-grade, client-facing applications in agile environments. This experience strengthened my foundation in clean code practices, system design, and structured problem-solving, as well as my ability to collaborate effectively with business and technical stakeholders across different domains. What distinguishes me is my ability to bridge AI, software engineering, and real-world application. I am comfortable working across data, models, and systems, and I value clear communication, analytical rigor, and measurable impact. I am particularly interested in roles where AI and data science are applied pragmatically to solve real-world problems and create sustainable value.…I am a Master’s graduate in Cognitive Systems from Ulm University with a strong focus on applied artificial intelligence, data-driven systems, and production-ready machine learning. My background combines hands-on AI engineering, software development, and close collaboration with domain experts to translate complex requirements into reliable, scalable solutions. During my internship with the Data Science team at Liebherr Group, I have worked on multiple AI projects spanning the full lifecycle from data ingestion and preprocessing to model development, evaluation, and deployment preparation. My work includes building pipelines to extracting information from unstructured technical data, developing RAG-based chatbots for efficient document search, and contributing to computer vision solutions for safety-critical environments. Across these projects, I have gained experience building and managing AI-based applications, as well as developing different approaches to designing and operating AI systems in practice. Previously, I worked for three years as a Software Engineer at PwC, where I developed and maintained production-grade, client-facing applications in agile environments. This experience strengthened my foundation in clean code practices, system design, and structured problem-solving, as well as my ability to collaborate effectively with business and technical stakeholders across different domains. What distinguishes me is my ability to bridge AI, software engineering, and real-world application. I am comfortable working across data, models, and systems, and I value clear communication, analytical rigor, and measurable impact. I am particularly interested in roles where AI and data science are applied pragmatically to solve real-world problems and create sustainable value.WWWW…