Aleksandr Shchegolev

I've built production AI systems for ВБ-digital and Gazprombank, from voice agents for collection calls to RAG-powered support automation. At ВБ-digital, I developed a voice agent that automated 70% of soft collection, with capacity equivalent to 25–30 employees. I also built hierarchical LLM classification, document-processing, and Airflow workflows for operational analytics. At Gazprombank, I improved a technical-support chatbot's operator transfer rate from 35% to 18% and reduced Customer Effort Score by 17%. I also developed debtor call automation and graph-based anti-fraud systems that increased detected real fraud by 13.5%. Earlier at beeline Russia, I deployed CatBoost models for default and churn prediction, improving churn accuracy from 68% to 82%. I work across Python, LLMs, RAG, NLP, ML infrastructure, data pipelines, and end-to-end product delivery.