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Adam is a software architect with over 15 years of hands-on development experience across demanding environments like banking compliance, healthcare, and robotics. Adam has designed and scaled systems from early MVPs to enterprise platforms, and successfully coordinated distributed offshore teams of up to 120 developers. He knows exactly when code needs to be elegant and when pragmatism wins to ship on time. Today, Adam trains engineering teams in Agentic Coding, focusing on Claude Code and GitHub Copilot CLI. His approach is built entirely on Spec-Driven Development. Having spent years translating business requirements into foolproof architectural guardrails for large, distributed teams, he applies the exact same discipline to AI. He knows that without strict boundaries and clear specifications, AI tools simply generate technical debt at unprecedented speed. Adam teaches developers how to treat AI like a high-speed junior developer: guided by rigorous specs, pragmatic architecture, and clear domain boundaries. He only teaches what he has personally deployed, debugged, and scaled in production. Through his hands-on training, teams learn to: * Write airtight specifications that drive AI-generated code predictably. * Review, manage, and refactor AI-assisted code to prevent technical debt. * Build pragmatic architectures they will still enjoy working on years down the line. "I look forward to seeing you in the course, let's build resilient, AI-assisted software together!" Adam

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.

Опытный инженер по машинному обучению и data scientist с обширной экспертизой в области автоматизированной обработки изображений и работы с неструктурированными данными. Катарина участвовала во множестве международных исследовательских проектов и получила степень Ph.D. в области умной микроскопии. В этот период она читала лекции и проводила семинары в одном из крупных университетов Германии, помогая студентам электротехники и мехатроники развивать ключевые навыки в области AI и deep learning. Инструменты и фреймворки: PyTorch, TensorFlow, scikit-learn, pandas, PySpark, NumPy, MLflow, MatLab, Snowflake, Amazon Web Services и Databricks

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.

Machine Learning Engineer и Software Architect с опытом работы более 15 лет, специализирующийся на deep learning, NLP и системах на основе LLM. Основное внимание уделяется проектированию и развертыванию сквозных AI pipelines, включая model fine-tuning (LoRA/PEFT), retrieval-augmented generation (RAG) и высокопроизводительный inference с использованием современных фреймворков, таких как PyTorch, Hugging Face Transformers и vLLM. Опыт в создании agentic systems с использованием LangChain и LangGraph, а также в разработке масштабируемых backend services на Python. Сочетает прочные принципы software engineering с практическим опытом в data-centric AI для предоставления надежных, готовых к производству решений.