NLP Engineer crafting efficient tooling for large language models.
NLP / ML engineer and researcher focused on tokenizer adaptation, LLM fine-tuning, and evaluation.
I iterate quickly on open-source model stacks, building efficient training and inference workflows for multilingual use-cases. I enjoy collaborating with cross-functional teams and translating research insights into production impact.
Balanced toolkit across research, engineering, and deployment for large language models.
Hands-on roles delivering large language model research, productionization, and tooling.
Library for separating and reusing embedding layers in large language models.
Telegram assistant that manages schedules and summarizes news with LLM-powered reasoning.
Researching tokenizer adaptation and cost-efficient strategies for large language models.
Investigates how alternative tokenizations of the same text impact LLM inference quality.
Proposes cost-effective adaptation approach for improving the performance of LLM generation in target language.
Demonstrates tokenizer adaptation as a cost-effective technique by analyzing text quality and token efficiency across diverse benchmarks.
Grounded in data analysis, software systems, and human-centered AI design.
Let’s collaborate on tokenizer research, LLM evaluation, or productizing AI workflows.
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