July 2026 – Present
Founder & AI/ML Engineer
Building custom AI software and ML-enabled systems, with responsibility for technical design, implementation and selected applied products.
About
From quantitative research to applied AI systems.
I am an AI/ML Engineer and founder of JadayLix Studio. My path spans physics and computational neuroscience, data science and ML engineering, and now GenAI, RAG and agent workflows.
My experience combines computational research with applied machine learning. Bayesian modeling and nonlinear systems inform how I approach experiments, evaluation and engineering limitations.
Experience
August 2023 – Present
AI / Machine Learning Engineer
Independent / Freelance Projects
Independent engineering projects exploring LLM applications, retrieval, agent workflows and ML systems. This work continues alongside JadayLix Studio.
March 2023 – July 2023
Machine Learning Engineer
The Capital Markets Company GmbH / Capco
Developed a sentiment-analysis model, addressing imbalanced classification with SMOTE and class weighting. Worked with Azure ML deployment, Azure Key Vault and business stakeholders.
July 2022 – September 2022
MLOps / DevOps Engineer Intern
Pexon Consulting GmbH
Worked on Flask/REST APIs, Docker and Azure Pipelines, with infrastructure using Terraform, AKS and Helm. Monitoring with Prometheus and Grafana.
2015 – 2022
Research Data Scientist
Central Institute of Mental Health (ZI Mannheim)
Computational neuroscience research using Bayesian modeling, neural models, fMRI, nonlinear systems and data pipelines. Peer-reviewed work on Dynamic Causal Modeling and social cognition.
Engineering stack
GenAI / Retrieval
Machine Learning
ML Systems / MLOps
Backend / Cloud
Education & credentials
Dr. rer. nat. / Physics
Heidelberg University · magna cum laude
Computational neuroscience, Dynamic Causal Modeling, fMRI and Bayesian modeling.
Machine Learning Engineer
DataScientest · 2024
Data Scientist
DataScientest · 2024
Microsoft Certified: Azure Fundamentals
Microsoft
Selected publications
Peer-reviewed research in computational neuroscience.
Social Cognitive and Affective Neuroscience · 2022
Effective connectivity of the human mirror neuron system during social cognition
Examined effective connectivity during social-cognition tasks using fMRI, Dynamic Causal Modeling and Bayesian model selection.
Frontiers in Neuroscience · 2020
Dynamic Causal Modeling for fMRI With Wilson-Cowan-Based Neuronal Equations
Introduced nonlinear Wilson–Cowan equations for Dynamic Causal Modeling and compared them with bilinear models using empirical and synthetic fMRI data.