Nuestros Formadores Expertos

Adam Furmanczuk

he/him

Software Architect & Pragmatic Engineering Coach

Software architect with over 15 years of hands-on development experience across highly regulated industries including banking compliance, border control, healthcare, and robotics middleware. Adam has designed and scaled systems from early MVPs to enterprise platforms, coordinating distributed teams of 120+ developers across the DACH region. He knows exactly when code needs to be elegant and when pragmatism wins to ship on time. He specializes in pragmatic, production-ready architectures using Java, C#, Angular, Flutter, Docker, AWS, and arc42/DDD methodologies. He only teaches what he has personally deployed, debugged, and scaled in production. His approach focuses on real code over theory. Teams learn to identify over-engineering, manage technical debt effectively, and build architectures they will still enjoy working on years down the line. Pragmatic, production-ready, and directly applicable.

Björn Wilmsmann

he/him

AI-based automation, AI-augmented software engineering, Spring, Java, web technologies

Independent IT consultant, trainer, and published author with deep expertise in Java, Spring Boot, and cloud-native architectures. As co-author of "Stratospheric: From Zero to Production with Spring Boot and AWS," Björn has helped thousands of developers navigate the path from local development to production-ready cloud deployments. He specializes in digital transformation, helping businesses automate processes, improve software quality, and make critical data accessible through well-designed APIs. His client work spans enterprise application development, architecture and design reviews, Angular and Spring Boot training, and hands-on coaching of development teams across industries. Björn brings a pragmatic, business-focused approach to technology. Whether advising on architecture decisions, conducting software quality audits, or running customized workshop formats, he focuses on delivering measurable impact and enabling teams to work independently with new technologies.

Katharina Schmidt

she/her

Ingeniera de Machine Learning y Científica de Datos | Experta en Computer Vision

Ingeniera de machine learning y científica de datos con amplia experiencia en procesamiento automatizado de imágenes y trabajo con datos no estructurados. Katharina ha participado en numerosos proyectos internacionales de investigación y obtuvo su doctorado en el campo de la microscopía inteligente. Durante ese tiempo, impartió conferencias y seminarios en una importante universidad alemana para ayudar a estudiantes de ingeniería eléctrica y mecatrónica a desarrollar competencias clave en IA y deep learning. Herramientas y frameworks: PyTorch, TensorFlow, scikit-learn, pandas, PySpark, NumPy, MLflow, MatLab, Snowflake, Amazon Web Services y Databricks

Marcus Nitschke

he/him

IT-Projektconsultant

IT project consultant and Product Owner with a passion for products that deliver real value. Thanks to his many years of experience in the digitalisation of critical processes, he knows exactly how to translate requirements precisely and guide stakeholders confidently through technological transformation projects. With a background in engineering and a deep understanding of complex IT architectures, he acts as a bridge between business, product, and development. As a trainer, Marcus stands out through his ability to combine this hands-on expertise with agile methodologies, enabling teams to develop user-centred and technically sound solutions with real impact.

Pawel Sawicki

he/him

Ingeniero Senior de Machine Learning

Ingeniero de Machine Learning y Arquitecto de Software con más de 15 años de experiencia, especializado en deep learning, NLP y sistemas basados en LLM. Fuerte enfoque en el diseño y despliegue de pipelines de IA de extremo a extremo, incluyendo el fine-tuning de modelos (LoRA/PEFT), la generación aumentada por recuperación (RAG) y la inferencia de alto rendimiento con frameworks modernos como PyTorch, Hugging Face Transformers y vLLM. Experiencia en la construcción de sistemas agénticos utilizando LangChain y LangGraph, así como en el desarrollo de servicios backend escalables en Python. Combina sólidos principios de ingeniería de software con experiencia práctica en IA centrada en datos para ofrecer soluciones robustas y listas para producción.