AI Engineer & Researcher
Building intelligent systems at the intersection of ML, NLP, Computer Vision, and Agentic AI. Pursuing M.Sc. Cognitive Systems (AI) at Universität Potsdam.
"I'm an AI Researcher driven by a small goal: to engineer technology that solves problems and make people's lives a little easier. I focus on healthcare, autonomous systems, research, and also enterprise applications. I'm always motivated by the opportunity to develop AI systems that gives a value & support human progress."
A production-ready AI assistant that retrieves, reasons, and responds using a multi-tool agentic pipeline deployed on Azure with automated CI/CD via GitHub Actions.
A post-hoc XAI framework for autonomous driving using EfficientNet B0, and human-in-the-loop learning to predict and audit 5 vehicle actions with continuous feedback retraining.
An end-to-end conversational AI system that speaks to customers in real-time via phone calls. Integrates speech-to-text, retrieves relevant knowledge via RAG, generates intelligent responses with LLMs, and converts them back to natural speech — all with sub-second latency.
An accessibility AI system with two modules: a Vision Transformer-based scene understanding pipeline that narrates surroundings for visually impaired users in real-time, and an audio-to-sign-language translation system that converts spoken audio into sign language gestures for deaf and hard-of-hearing individuals.
A multi-agent AI system that audits a raw data lake, designs a cleaning plan, reviews it through a committee (agents + human), generates & validates cleaning code, executes it, and produces a final quality report.
A no-code Streamlit app for LLM knowledge distillation — annotate any dataset with a large "teacher" model, then evaluate or fine-tune smaller "student" models via zero-shot, few-shot, or full weight updates, all from a single UI.
Medical AI framework achieving AUC > 0.98, comparing CNNs and Vision Transformers. Grad-CAM faithfulness and concentration metrics build clinician trust in AI-assisted radiology.
Multi-agent Analyst–Architect–Coder workflow using CrewAI and FAISS that transforms academic PDFs into deployable software/research projects.
Privacy-preserving code review using LangGraph, RAG, and local LLMs where no code leaves your machine. Autonomously identifies bugs, security risks, and performance issues in GitHub repositories.
Open to research collaborations, AI/ML roles, and interesting projects.
Based in Berlin - available globally.