GitHub Experts in Nuremberg

• Hands-on support for mid-market M&A projects: company analysis, client briefings and information memoranda
• Set up structured project, data room and documentation processes, and prepared decision-making materials
• Designed and built the company website using AWS, and set up tracking and retargeting
• Practical use of AI applications and coding agents for research, analysis, documentation and software development

• Fine-tuned and evaluated alternative backbone architectures for the AFFGAN handwriting style encoder to better capture writer-specific features, benchmarking against the original baseline architecture and replacing the legacy VGG19 architecture with a custom Transformer-based encoder.
• Extended the system beyond word-level synthesis to line-level handwriting generation, integrating and adapting an existing line-generation architecture to support full-sentence output.
• Deployed the handwriting generation pipeline as a web application, enabling users to select a writing style, input custom text, and instantly generate a corresponding handwriting image.
• Integrated TrOCR into a teacher–student distillation framework to evaluate and improve readability of generated handwriting, improving OCR accuracy by 20% over the baseline.
• Boosted user-study pick-rates by 40%, demonstrating a significant leap in the perceptual quality and realism of the generated handwriting compared to the baseline model.

Tech: PyTorch, Python, GANs, CNN, ViT, TrOCR, Streamlit, CUDA.

Built charts for historical data, alarms, and downtime, plus enhanced interactive tables to efficiently track and compare oil-well performance. Eliminated 80% of high/medium security risks (Checkmarx) for the app. Optimized charting libraries, cutting usage from to 300–400MB for large data sets.

• Delivered 40+ AI projects and 23 strategic consultations for international clients (US, Europe, Middle East), achieving a 98% job success rate and building long-term partnerships.
• Developed and deployed production-grade AI solutions in computer vision, NLP, deep learning, and generative AI (LLMs, RAG pipelines, Stable Diffusion, OCR, chatbots), enabling automation and improving client efficiency by up to 70%.
• Designed and fine-tuned large language models (LLMs), including prompt engineering and integration with enterprise knowledge bases, leading to smarter decision-making and reduced manual effort.
• Built real-time computer vision applications (detection, segmentation, OCR) and integrated them into business systems, significantly enhancing accuracy and scalability.
• Consulted startups and enterprises on AI strategy, architecture, and deployment (cloud & on-premise), accelerating product development and reducing time-to-market.
• Managed complete AI project lifecycles (requirements gathering, solution design, deployment, support) in agile, international, and cross-functional environments, ensuring high-quality delivery.

• Lead software architect and technical team lead for a new logistics platform for load carrier trade
• Core design of software architecture using arc42 spanning frontend, backend, delivery strategies and cloud native infrastructure with Domain Driven Design and Hexagonal Architecture
• Evaluation of initial business requirements and software development roadmap
• Technical team lead in a Scrum team of 9 people
• Introduced and strengthened AI-assisted (JetBrains AI & GitHub Copilot) and collaborative code development strategies to speed up feature development

• Analysis of the role of marriage as an informal insurance in Germany using econometric methods (supervisor: Prof. Dr. Harald Tauchmann).
• Prepared and cleaned multiple panel datasets and extracted relevant variables to create a final panel dataset with over 36,000 observations (2006–2020, SOEP).
• Designed an IV model in Stata to examine the causal link between couple separation and health shocks (mental/physical).
• Conducted robustness checks to ensure stability and validity of the results.

• Technology update
• Microsoft Azure certification

• Building a Data Analytics platform for Karl Storz
• Generic development of all ETL processes
• Preparing and providing data in Databricks Delta tables for reuse in Databricks Machine Learning
• Technologies & Tools: Azure Data Factory, CI/CD Pipeline with GitHub DevOps, Python, Azure Databricks (Unity Catalog), T-SQL

• Integrated MarianMT (Marian Machine Translation) models to ensure high-quality neural machine translation (NMT).
• Managed model loading and tokenization via Hugging Face Transformers, optimizing for offline caching and reproducibility.
• Planned extensions: language auto-detection, batch translations, and streamlined GPU inference with PyTorch.

• Development of a new innovative payment product for the European market with the aim of establishing an alternative to the existing global players.
• Set-up and scaling of the software development processes and teams.
• Architect and implementation of the business processes in an event sourcing microservice architecture.
• Implementation on a prototype in the context of the Digital Euro.
• Technologies: Ktor (Kotlin), AWS Managed Kubernetes with Fargate, MongoDB, React, ElasticSearch, KoTest, Gradle, Agile Development, Pair Programming, GitHub Actions, Gradle, SonarQube

• Intent-driven approach to configure all models across all series automotive in all distribution markets of this car manufacturer
• Developed a customer-facing, conversation-driven integration layer to achieve 100% hallucination-free technical configurations
• Utilized Microsoft Azure AI Services: AI Foundry, Agent Service, AI Search; Prompt Shield Services; Content Security; Terraform; API Gateway; AI Gateway; Container Services; Azure Agent SDK; Agent Skills; RAG; MCP Servers and tools

• Built features using Jetpack Compose
• Improved location-based features with in-memory caching
• Developed Merchant-to-Merchant chat functionality from scratch
• Integrated FCM with deep links to enable contextual push notifications
• Performance tuning for Google Maps view using clustering for better performance in dense areas

• Analysis of software quality, technical debt, and development processes
• Derivation of sustainable improvements for developer experience, productivity, and system stability

• Focused on exploring the intersection of Music and AI.