Machine Learning & Software Engineer
I am a master’s student in Artificial Intelligence at Warsaw University of Technology with a strong foundation in machine learning, data science, and software development, and a passion for AI in finance. I currently work as an AI Engineer at AI Clearing, building multi-agent AI assistants and production RAG systems for complex analytical workflows. I have authored papers on advanced models, including “Applying Informer for Option Pricing” (ICAART 2025), “Options Pricing Platform with NNs, LLMs & RL” (ACIIDS 2025, Best Student Paper), and “DeltaHedge: A Multi-Agent Framework for Portfolio Options Optimization” (PACIS 2025, A-tier). I also completed the EPAM Data Science Program in deep learning and MLOps and received a ministerial scholarship for research development.
Major: Artificial Intelligence
Major: Bachelor of Engineering in Automatic Control & Robotics
Accepted to PACIS (A-tier per CORE). Presents a multi-agent RL framework that ensembles options-based hedges to improve risk-adjusted returns and stabilize portfolios in volatile markets.
Integrated neural networks, volatility modeling, and sentiment analysis via LLMs into a hybrid option pricing model. Designed an RL trading strategy. Honored with Best Student Paper Award.
Compares Informer, Autoformer, FEDformer, and Pyraformer for option valuation, short- and medium-term forecasting, and rule-based trading. Benchmarks against classical models and deep sequence baselines across equities, indices, and crypto options, showing consistent gains in pricing accuracy and forecast robustness.
In this work, I explored Informer—a Transformer-based model—for option pricing, adapting it to this specific task and evaluating it against classical models.
Developing a multi-agent AI assistant in a web app using LangGraph and LangChain to enable conversational analysis of complex datasets. Own end-to-end components including a production-ready RAG pipeline and human-in-the-loop interactions. Improved benchmark accuracy from 78% to 98%, cut latency from ~80s to ~20s, reduced token usage from ~1.1M to ~200k via caching and selective LLM access, and integrated SMS/WhatsApp workflows.
Developed and maintained software applications using Python and JavaScript; analyzed market trends and optimized campaign strategies.
34-week intensive training on ML, DL, MLOps (Docker, MLflow) – average grade: 85%
Built React & .NET micro-frontends, implemented REST APIs, and automated CI/CD pipelines on Azure DevOps in cross-functional Agile teams.
Founder of a student research group focused on generative AI, large language models (LLMs), NLP, cognitive architectures, multi-agent systems, and argument modeling. Promotes practical applications in finance, economics, social sciences, and software engineering.
Led AI/LLM-oriented student projects, organized tech-business meetups, and managed a 10-member team delivering workshops and hackathons in Warsaw.
Collaborated on entrepreneurship projects addressing social challenges through innovation, with emphasis on leadership and cross-functional teamwork.
Coordinated school-wide events and represented students in leadership roles at the high school level.
Organized a volunteer-based environmental campaign that successfully engaged locals in cleaning a forest park. Developed leadership and event management skills.
Recipient of the “Wsparcie studenta w kontynuacji rozwoju naukowego w obszarze kognitywnych architektur i wieloagentowych rozwiązań stosowanych w inteligentnych systemach informacyjnych” project grant. The project focuses on integrating LLMs with multi-agent architectures, building research platforms, and preparing publications (e.g., AAMAS, ICML) on AI applications in finance and decision systems.
Associated with Warsaw University of Technology. Received for paper “Options Pricing Platform with Neural Networks, LLMs and Reinforcement Learning.”
Honored at the Foundation for the Promotion and Accreditation of Economic Education for outstanding entrepreneurial achievements.