About
Building scalable AI platforms and machine learning systems, from production data infrastructure to genomic medicine, at the intersection of AI engineering, cloud platforms, and biomedical research.
As a Data Scientist & ML Engineer at scieneers, I design and build scalable data platforms and machine learning systems for organizations across a wide range of industries, including healthcare, energy, construction, media, and the public sector. My work spans the full lifecycle of modern AI systems, from data engineering and model development to cloud architecture, MLOps, and production deployment.
In parallel, I pursue a PhD at the Centre for Human Genetics and Genomic Medicine at RWTH Aachen University Hospital. My research focuses on developing and applying machine learning methods for genomic medicine. Our work explores a range of approaches for genomic analysis, including the use of Genomic Language Models (gLMs) and Large Language Models (LLMs) to investigate previously understudied regions of the human genome, often referred to as the “dark genome”. Example applications range from predicting protein structures and estimating the pathogenicity of genetic variants to investigating the biological role of microproteins.
Moreover, we push the state of the art by developing multimodal models for genomic medicine, for example by fusing LLMs with gLMs to open a natural language based interface on how we will interact with genomic data in the near future. In addition, we are currently developing foundation models for genomic medicine.
To support this work, I design and operate a cloud-based research platform that enables scalable genomic analysis pipelines and large-scale ML experimentation.
Alongside my work in industry and research, I regularly speak at conferences and technical events on topics including AI infrastructure, machine learning in production, and AI applications in genomics and healthcare.
What I share
New tools, learnings, and implementation insights across:
- Genomics × AI, with a special interest in healthcare and biotech
- Cloud & data platforms and infrastructure, built to scale across domains
- Machine Learning Engineering: classical ML models, LLMs, and gLMs, with MLOps, LLMOps, and data engineering and science