Mikhail Knyazev is a Machine Learning Engineer with 11 years of experience building practical, production-ready ML and backend systems from news clustering and multi-label classifiers to rule-based entity linking. He blends a strong physics and applied mathematics background (MIPT) with hands-on software craftsmanship—clean code, reproducible experiments, and high test coverage are his trademarks. At Interfax he shipped scalable RESTful services, an online topic-tracking pipeline, and a fast adaptive diff algorithm for million-character texts; at Incode he continues to apply ML in production. Comfortable with both deep learning and pragmatic rule-based approaches, he values practicality over purity and often complements neural models with deterministic systems. Based in Belgrade, he also has experience building hardware-integrated GUIs and precision lab control systems, highlighting an unusual mix of experimental physics and software engineering.
11 years of coding experience
11 years of employment as a software developer
Bachelor's degree, Applied Mathematics and Physics, 4.3, Bachelor's degree, Applied Mathematics and Physics, 4.3 at Moscow Institute of Physics and Technology (State University) (MIPT)
Telegram Bot to share music with Odesli (former Songlink) service.
Contributions:24 releases, 15 reviews, 247 commits in 3 years 4 months
telegram-botbottelegrammusicshare
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Mikhail Knyazev - Machine Learning Engineer at Incode Technologies