🎓 Artificial Intelligence student @ Poznań University of Technology
🧠 Focused on ML, Deep Learning, LLMs & real-world AI systems
🌍 Poland 🇵🇱

I'm a third-year Artificial Intelligence student with a strong engineering mindset.
I enjoy building AI systems end-to-end — from data collection and labeling to model training, evaluation, and deployment.

• 🧩 Strong focus on clean architecture, scalability & performance
• 🧪 Passion for state-of-the-art AI research & experimentation
• 🚀 Turning research ideas into production-ready AI systems

Hands-on experience with:

• 📊 Data preprocessing, feature engineering & EDA
• 🧠 Training deep neural networks (CNNs, RNNs, Transformers)
• 🔍 Model evaluation, optimization & debugging
• 🧪 Reproducible ML experiments & benchmarking

• 🧠 Large Language Models — prompting, embeddings, RAG, agent concepts
• 🗣️ Natural Language Processing — tokenization, transformers, semantic search
• 👁️ Computer Vision — image processing, feature extraction, object detection
• 🎨 Generative AI — text, image & representation learning
• 🧬 Multi-modal AI — text + vision systems
• 🎯 Supervised & Unsupervised Learning
• 🧠 Representation Learning
• 🔁 Sequence Modeling & Time Series
• 🎮 Reinforcement Learning
• 📐 Optimization & loss design

• 🤗 Hugging Face models, datasets & pipelines
• 🧠 LangChain for LLM-powered applications & agents
• 📊 Kaggle for datasets, experimentation & benchmarking
• 🎛 Streamlit for rapid ML app prototyping

• ⚛️ Frontend apps with React + TypeScript
• 🔗 APIs & backend logic (Node.js / PHP)
• 🧩 Clean, modular & testable code
• 🎯 Strong focus on maintainability

• 🧮 SQL: MySQL, PostgreSQL, SQLite
• 📦 NoSQL: MongoDB, Cassandra
• ⚡ In-memory & caching: Redis
• 🧠 Data modeling for analytics & ML pipelines

• 🐳 Docker for reproducible ML environments
• 🔁 Git & GitHub for versioning and collaboration
• 🧑‍💻 Linux-based development & automation
• 🕷️ Selenium for Web scraping & crawling for dataset creation
• ☁️ Fundamentals of ML deployment & serving

• 🤖 LLM-based systems & AI agents
• 🧠 Deep Learning architectures
• 🏗 Full-stack AI applications
• ☁️ Scalable ML deployment & MLOps practices

• 💼 LinkedIn
• 📁 GitHub Repositories