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🎓 Student, Faculty of Computational Mathematics and Cybernetics, MSU — Department of Mathematical Statistics;
🔭 Currently focused on Data Analysis with a roadmap to Data Science;
🌐 Fluent with production pipelines, statistical modeling, and ML experimentation.
I am a Machine Learning / AI Engineer with a strong background in mathematical statistics (MSU).
My work focuses on building LLM-based systems and integrating machine learning models into real-world backend pipelines.
Key areas of interest:
- LLM systems (RAG, multi-agent architectures)
- AI for Science (automation of research workflows)
- probabilistic methods in ML
I combine strong theoretical foundations with hands-on experience in:
- ML systems design
- backend development (FastAPI)
- production ML pipelines
Currently interested in research-oriented AI systems and advanced LLM applications.
I focus on designing end-to-end ML systems, from data processing to model inference and deployment.
LLM systems, AI for Science, multi-agent systems, probabilistic ML
📋📩👨💼To look my CV: click here
💻 I love writing code and analyse the data, then retrieve insights from it.
💬 Ask me anything about from Here
📫 How to reach me: boris.cherkasov@outlook.com
- FastAPI + YandexGPT
- compliance score for clinical protocols
- 1st place — Sechenov University Case Championship
- embeddings + FAISS retrieval
- improved relevance by 8–10%
- production-ready pipeline
- NLP + automation
- parsing reports + sprint management
- reduced manual work from hours to seconds
- Data processing, statistical analysis, and experiment design (A/B testing);
- Production dashboards & BI instrumentation (Power BI, Superset, Qlik);
- A/B test design, analysis, and interpretation using statistical methods;
- ML model development, hyperparameter tuning (Optuna), and model evaluation;
- Time series forecasting, anomaly detection, and interpretable ML;
- MLOps fundamentals: Dockerized models, reproducible pipelines, basic model serving (FastAPI).
