AI Engineer & Research Engineer
I am a master’s student in Artificial Intelligence at Warsaw University of Technology and an AI Engineer at AI Clearing, where I build production LLM systems, RAG pipelines, and multi-agent architectures for complex analytical workflows. My research focuses on multi-agent systems, quantitative finance, option pricing, portfolio optimization, and verifiable AI decision-support systems. I have authored and co-authored publications accepted at venues including ICCS, PACIS, ACIIDS, ICAART, and AAMAS workshops, with ongoing work under review at NeurIPS 2026. I am particularly interested in robust, auditable, and scalable AI systems for decision-making, retrieval, and quantitative applications.
Major: Artificial Intelligence • GPA: 4.60/5.0
Thesis: Investigating the Application of Game Theory in LLM-based Multi-Agent Systems
Major: Automatic Control & Robotics
Thesis: AI Architectures for Option Pricing and Portfolio Management: Transformers to MAS
Proposes a PPO-compatible critic design with a shared team baseline and zero-mean agent-specific residuals to improve credit assignment in cooperative multi-agent reinforcement learning.
Introduces Explanatory Equilibrium as a design principle for explanation-aware MAS. Models LLM communication as a signaling game to derive validation bounds preventing cheap-talk degeneration.
Introduces a retrieval-augmented multi-agent framework for portfolio decision support, combining historical precedent retrieval, structured allocation decisions, and risk-aware evaluation.
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.
Compares Informer, Autoformer, FEDformer, and Pyraformer against classical models and deep sequence baselines across equities, indices, and crypto options.
Explored the Informer—a Transformer-based model—for option pricing, adapting it to handle long-sequence modeling capabilities to enhance prediction accuracy.
Built a research-oriented market-making simulation framework based on inventory-aware quoting, partial fills, fees/slippage, risk controls, calibration utilities, and reproducible strategy benchmarking.
Developed an event-level order-book research pipeline for short-horizon mid-price prediction under latency, transaction cost, and anti-leakage constraints, with causal features, chronological validation, and post-cost trading diagnostics.
Architected and deployed a production multi-agent LLM system (LangGraph, LangChain). Designed a domain-specific RAG pipeline handling complex construction data (Excel, nested tables, marked-up PDFs) via OCR, vision LLMs, BM25, and reranking.
Impact: Increased system accuracy from 78% to 98%. Reduced latency (80s → 20s) via caching and token usage (1.1M → 200k) via selective tool-based routing.
Developed Python-based automation tools and data pipelines. Analyzed market trends and optimized strategies to support business operations.
Built React (TypeScript) and .NET micro-frontends, implemented REST APIs, and integrated backend logic in cross-functional Agile teams.
Leading a student research group focused on generative AI, multi-agent systems, LLM applications, and argument modeling. Promoting practical AI applications in finance and decision systems.
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.
Received research funding to support the development of multi-agent architectures and cognitive systems applied to intelligent information networks.
Awarded for outstanding academic achievements and continued scientific development in the area of cognitive architectures and MAS in intelligent systems.
Honored for the paper "Options Pricing Platform with Neural Networks, LLMs and Reinforcement Learning" at the Asian Conference on Intelligent Information and Database Systems.