About
Highly skilled Machine Learning Engineer and MSc Data & Computer Science candidate with expertise in designing and deploying advanced Large Language Model (LLM) and Natural Language Processing (NLP) solutions. Proven ability to leverage Transformers, RAG, and production-ready ML pipelines (PyTorch, Hugging Face) to deliver scalable, high-impact AI applications. Adept at bridging research and deployment, driving innovation in applied NLP, conversational AI, and speech technology.
Work
Heidelberg, Baden-Württemberg, Germany
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Summary
Conducted advanced research in deep neural networks and variational inference to improve model generalization for regulatory region tasks at a leading cancer research center.
Highlights
Integrated a Foundational model using Borzoi-generated embeddings to train a deep neural network, significantly improving model generalization across diverse regulatory region tasks.
Developed a modular guide class (AmortizedNormal) to seamlessly combine LLM embeddings with variational inference in NumPyro, streamlining complex model architectures.
Performed scalable Stochastic Variational Inference (SVI) on over 20,000 DNA sequences, boosting ELBO convergence speed by ~30%.
Weinheim, Baden-Württemberg, Germany
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Summary
Led the development and deployment of GenAI systems, focusing on real-time RAG pipelines and LLM-based solutions for knowledge extraction.
Highlights
Built and deployed a real-time Retrieval-Augmented Generation (RAG) pipeline with FastAPI, Sublime, and LangChain, retrieving enterprise knowledge from over 10,000 records.
Collaborated cross-functionally on internal GenAI initiatives, including LLM-based solutions, to enhance knowledge extraction capabilities.
Conducted user interviews, documented workflows, and facilitated workshops with cross-functional teams to gather requirements and ensure alignment for AI applications.
Developed comprehensive test cases and automated evaluation pipelines for pilot AI applications, ensuring robust performance and reliability.
Synthesized complex technical and business feedback into structured reports to guide strategic deployment and iterative improvements.
Heidelberg, Baden-Württemberg, Germany
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Summary
Analyzed patient-question interactions and enhanced predictive accuracy through advanced probabilistic modeling and data visualization techniques.
Highlights
Analyzed 15,000 patient-question interactions using advanced probabilistic response models to derive critical insights.
Improved predictive accuracy by 18% compared to baseline cognitive models, demonstrating enhanced model performance.
Utilized Seaborn and Matplotlib visualizations to conduct comprehensive model performance comparisons and present findings effectively.
Mannheim, Baden-Württemberg, Germany
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Summary
Managed and automated fMRI data preprocessing pipelines for a large patient cohort, significantly enhancing efficiency and data consistency.
Highlights
Processed fMRI data from over 15,000 patients leveraging BIDS pipelines, ICA, and BET for precise brain extraction.
Automated critical data preprocessing pipelines, reducing data preparation time by 50% and ensuring high consistency for research studies.
Education
Skills
Programming & Automation
Python, R, Shell scripting, REST APIs, Workflow orchestration.
AI/ML Frameworks
PyTorch, TensorFlow, NumPyro, LangChain, LangGraph, JAX, Deep Learning.
AI Systems
RAG pipelines, LLM fine-tuning, Agent-based automation, Predictive modeling.
Data Handling
Pandas, NumPy, SQL, NoSQL, Data preprocessing & visualization, Matplotlib, Seaborn.
Development Practices
Agile/Scrum, CI/CD, Git, Containerized environments (Docker).
Mentoring & Collaboration
Cross-functional training, AI adoption advocacy, Technical workshops.
Interests
Hobbies
Reading books, Football, Trekking.