Professional Overview
Applied AI engineer working across LLM ablations, RAG evaluation, and edge ML. Built a 14M-parameter decoder-only transformer ablation on RF text, a controlled RAG study with a golden eval set, and a voice-RAG assistant with FAISS and Groq. Currently Research Engineer at Huawei R&D Russia and M.Sc. student at Skoltech.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Applied PyTorch quantization and pruning for embedded ML deployment.
- Investigated Crest Factor Reduction across 4 RF front-end scenarios.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built voice-RAG assistant with FAISS, sentence-transformers, and Groq LLM.
- Trained 14M-param decoder-only transformer ablation on 9.45M RF text tokens.
- Ran controlled RAG ablation with golden QA set and reproducibility script.
Education
Field of Study: Applied Machine Learning and Wireless Communications
Areas of Expertise
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
