I'm an ML/AI Engineer with 3.5+ years of experience building production-oriented machine learning systems across computer vision, time-series modeling, and LLM applications.
Most of the production systems I have worked on are proprietary and cannot be published due to company confidentiality policies. My production contributions are maintained in internal GitLab repositories. This profile therefore highlights selected independent projects, educational materials, and open-source work that reflect how I design and build ML systems.
My main interests include end-to-end ML pipelines, agentic workflows, RAG, model serving, backend integration, and reliable production ML systems.
An AI-powered math tutor designed to guide users through problem solving rather than simply return an answer.
The project includes:
- LangGraph-based workflows for hints, guided solutions, and answer evaluation;
- a FastAPI backend;
- a PostgreSQL-backed repository of solved problems;
- support for open-source model serving with vLLM, TGI, and Ollama.
Tech stack: Python, FastAPI, LangGraph, PostgreSQL, vLLM, TGI, Ollama
A LangGraph-based assistant for university applicants and students.
The project combines program matching, required-document checklists, and a RAG assistant over official university admission rules and educational documents.
Tech stack: Python, FastAPI, LangGraph, RAG, PostgreSQL
A practical LLM engineering course that I designed and taught at ITMO University. It covers:
- transformer architecture and inference;
- KV cache, quantization, and inference optimization;
- open-source model serving;
- LangChain and LangGraph workflows;
- FastAPI-based LLM services;
- agentic applications and production-oriented LLM system design.
The repository contains the course structure, presentations, practical materials, and examples used in the program.
A practical Python course that I designed, authored, and delivered internally to employees at my company, with presentations, notebooks, runnable examples, quizzes, and exercises. It covers:
- operating-system and CPU fundamentals;
- multithreading and synchronization;
- multiprocessing, IPC, and process pools;
- asynchronous programming with
asyncio; - networking, WSGI, ASGI, and FastAPI-based asynchronous services.
Experiments with convolutional neural network architectures, computer vision, and seismic data denoising.
A collection of practical machine learning experiments covering classical ML, model evaluation, statistics, feature engineering, and experimental analysis.
I have contributed to BatchFlow, an open-source Python framework for machine learning pipelines, including computer vision tutorials, framework fixes, and tools for extracting intermediate neural network activations.