Kirill Gelvan is an AI agents researcher and NLP engineer with eight years of experience and a focused three-plus years in production NLP and RAG systems. Based in Munich, he built scalable retrieval-augmented pipelines and error-classification automation that reduced unethical responses by 35% and extended chatbot memory threefold while increasing ARPU. At JetBrains he developed an IDE Debugging-Agent and explored agent distillation and implicit context condensation to tackle software-engineering benchmarks. He combines hands-on model distillation, quantization and vector DB scaling with teaching experience—creating and delivering multiple university and Coursera courses—bringing both practical product impact and clear technical communication to cross-functional teams. Notably, he emphasizes team cohesion and confident leadership alongside technical depth, making him effective at turning research ideas into reliable engineering features.
8 years of coding experience
4 years of employment as a software developer
Master of Science - MS, Mathematics in Data Science, Master of Science - MS, Mathematics in Data Science at Technical University of Munich
Bachelor's degree, 𝐅𝐚𝐜𝐮𝐥𝐭𝐲 𝐨𝐟 𝐂𝐒, Applied Mathematics and Informatics, Bachelor's degree, 𝐅𝐚𝐜𝐮𝐥𝐭𝐲 𝐨𝐟 𝐂𝐒, Applied Mathematics and Informatics at Higher School of Economics
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