Nikita Golovko designs AI and industrial software architectures at Siemens. He focuses on taking machine learning models out of the lab and making them work reliably on the manufacturing shop floor.
He is an advocate for making AI systems architecturally visible. His work on arcAI42 extends the popular arc42 documentation framework to account for data dependencies, model drift, and feedback loops. By treating probabilistic AI as standard, maintainable infrastructure, he prevents learning models from becoming untraceable black boxes.
Nikita writes about system design and AI infrastructure on his blog.
