𝔏𝔢𝔳 𝔎𝔯𝔢𝔪𝔩𝔢𝔳

"𝔅𝔲𝔦𝔩𝔡𝔦𝔫𝔤 𝔱𝔥𝔦𝔫𝔤𝔰 𝔣𝔯𝔬𝔪 𝔰𝔠𝔯𝔞𝔱𝔠𝔥 𝔲𝔫𝔱𝔦𝔩 𝔱𝔥𝔢𝔶 𝔴𝔬𝔯𝔨. 𝔚𝔢 𝔞𝔯𝔢 𝔫𝔬𝔱 𝔶𝔬𝔲𝔯 𝔨𝔦𝔫𝔡𝔰."

ML Engineer focused on LLM systems, AI agents and deep learning.
MIREA — Artificial Intelligence & Machine Learning. Hackathon finalist. Building transformers on JAX for fun.

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⚔️ About

I build end-to-end ML systems — from raw data and feature engineering to production inference and deployment. Currently deep into LLM internals, agent architectures and RL theory.

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𝔓𝔯𝔬𝔧𝔢𝔠𝔱𝔰

🏦 High-Load RAG Pipeline — Alfa-Bank & MIPT Hackathon

Production RAG system (~7000 queries) optimized for BERTScore-Recall-L

• FAISS (bge-m3) + BM25 + RRF · cross-encoder reranking
• vLLM continuous batching · 2×T4 GPU-aware architecture
• Latency reduced by ~25%
• → github

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🛡️ PII Leak Detection — ARENA DATA (Top 6 / 25 teams)

ML pipeline for personal data leak detection

• 3-tier architecture: rule-engine → classification → scoring
• TF-IDF + LogReg · CatBoost with contextual scoring
• 35% FP reduction
• → github

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🎯 Retail Video OCR Pipeline — Lenta CV Hackathon

Production CV system for price tag extraction from video stream

• YOLOv8 + custom IoU tracker · PaddleOCR → EasyOCR cascade
• Throughput: 7 → 19 FPS · OCR inferences reduced by 22%
• → github

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📈 Retail Turnover Forecasting — X5 Tech Gradient (Final)

Predictive model for store RTO forecasting · 90.45 score

• Time-series feature engineering · gradient boosting · overfitting control
• → github

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⚙️ BPE Tokenizer from scratch

Byte-level BPE tokenizer · GPT-2 regex · O(n log n) complexity

• Built for deep understanding of LLM tokenization internals
• → github

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🔬 LLM from Scratch — JAX

Transformer implementation: Self-Attention · Multi-Head · MoE · MLA

• Researching memory efficiency and inference optimization
• RAG experiments on custom architecture — ongoing

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𝔖𝔱𝔞𝔠𝔨

stack = {
    "ml / dl":   ["PyTorch", "JAX", "CatBoost", "XGBoost", "Scikit-learn"],
    "llm / nlp": ["vLLM", "LangChain", "LangGraph", "FAISS", "BM25"],
    "infra":     ["Docker", "FastAPI", "SQL", "Git"],
    }

𝔉𝔦𝔫𝔡 𝔪𝔢