A bit about me.

I'm a Computer Science student at the Technical University of Munich, specializing in AI and software engineering. My career started where research meets industry — and I've kept that intersection as my operating principle.

At BCG Platinion, I contribute to AI strategy for the energy sector, designing architectures, running feasibility assessments, and building solutions that consultants use with clients.

At Allianz SE, I developed GenAI applications for insurance operations, from customer communication analysis to agentic workflows, and presented frameworks that made AI more accessible to non-technical teams.

At the Fraunhofer Society, I built a private RAG pipeline for institutional knowledge management, with a focus on security, accuracy, and guardrail design.

My published research on LLMs has been cited over 130 times, and my bachelor's thesis addresses one of the most critical challenges in production AI: mitigating hallucinations before they happen.

I'm drawn to problems where AI needs to be trusted, not just accurate. Regulated industries — insurance, energy, healthcare — where a wrong output has real consequences. That's where interesting engineering happens.

Beyond client work, I'm fascinated by multi-model orchestration, hallucination mitigation, and building AI systems that explain their reasoning. My personal project, Quetzal, is an AI-native learning platform that combines multi-model AI with pedagogical theory. It's where I push my own limits.

I don't believe in AI for AI's sake. Every system I build must answer three questions: Does it solve a real problem? Can the people who need it actually use it? Will it still work when I'm not in the room?

I work iteratively, communicate proactively, and design systems with the assumption that they need to be understood, maintained, and trusted by people who didn't build them.

When a project isn't right for AI, I'll say so. Honestly and early. That clarity is more valuable than any prototype.

I'm building toward a career at the intersection of AI research and applied engineering. Whether that's in enterprise AI, AI safety research, or building my own products, the common thread is the same: make AI work reliably for people who depend on it.