Szymon.
Machine Learning Developer
AI student who loves building intelligent systems that learn and adapt. Whether it’s predicting outcomes, classifying patterns, or detecting anomalies, I create machine learning solutions that make an impact.
Hi, my name is
About Me
I’m an AI & Applied Data Science student at Breda University of Applied Sciences, passionate about building machine learning models that solve problems across different domains. While I currently work on LLM projects as a student assistant at BUAS, I’m most interested in general purpose ML algorithms like classification, regression, and prediction models that can be applied anywhere.
I enjoy the versatility of core machine learning techniques that aren’t tied to a specific domain. Whether it’s training models to classify images, predict time series data, or detect patterns in any type of dataset, I’m drawn to projects where I can experiment with different algorithms and see what works best. I’m particularly interested in exploring computer vision and robotics as new application areas for these techniques.
When I’m not coding or training models, you’ll find me at the gym, tinkering with hardware projects, or exploring new datasets to experiment with. I believe the best way to understand ML is by applying algorithms to diverse problems and learning from the results.
Technologies I work with:- Python
- TensorFlow
- LangChain
- PyTorch
- Pandas & NumPy
- Docker
- SQL
Experience
Apr 2025 - present
Commissioned by the university administration, I led the development of an AI-powered chatbot designed to enhance prospective student engagement and streamline the admissions process.
Key Responsibilities
- End-to-End Product Development: Designed, built, and deployed a conversational AI assistant to support students in program discovery, application tracking, and admissions FAQs.
- Dynamic Data Integration: Implemented web scraping and API-based data pipelines to ensure real-time accuracy of chatbot responses based on official university data.
- User Experience Design: Engineered inclusive and adaptive conversation flows tailored to various student profiles, including international applicants and those with diverse academic paths.
- Stakeholder Collaboration: Conducted requirement-gathering sessions with academic and administrative staff to identify pain points and translate them into technical features.
Aug 2023 - Sep 2023
Trained in Security Operations Center operations, working with ELK Stack (Elasticsearch, Logstash, Kibana) and SIEM systems for security event monitoring and threat detection. Gained practical experience in log analysis and incident triage workflows.
Education
2024 - Present (Expected Graduation: 2028)
Bachelor of Applied Data Science & Artificial Intelligence
Brefa University of Applied Sciences, Breda, The Netherlands
GPA:
4.0 out of 4.0
2019 - 2024
IT Technician - Technical Secondary School
Technikum Lotnicze, Katowice, Poland
GPA:
~3.5 out of 4.0 (estimated due to grading system differences)
Completed technical education with focus on IT infrastructure, programming fundamentals, and computer systems. Gained hands-on experience through apprenticeships at local IT firms.
Projects
NLP
PyTorch
Transformers
Emotion in TV Shows
Built an NLP pipeline that detects and classifies emotions in TV content. Uses Transformer models (BERT, RoBERTa) to classify emotions across six categories with an F1 score of 0.75.
Computer Vision
TensorFlow
Transfer Learning
XAI
AI Powered Wall Plug Defect Detection System
Developed a computer vision system for automated quality control in manufacturing. Achieved 100% accuracy using transfer learning with MobileNetV2 and EfficientNetB0, with Explainable AI features for transparency. Comparable to $15,000+ commercial systems while operating at 10% of the cost on low-cost hardware.
Power BI
Business Analytics
Data Visualization
IKEA Breda POS Terminal Deployment Analytics Dashboard
Built a Power BI dashboard analyzing deployment trends for 500+ POS terminals. Identified patterns in re-deployments, employee performance, and operational gaps. Provided actionable recommendations that improved compliance rates by tracking terminal lifecycle events and detecting deployment inconsistencies.
Get In Touch
I’m always interested in hearing about new opportunities, research collaborations, or exciting AI projects. Whether you have a question, want to discuss machine learning, or just want to connect—feel free to reach out!
Copied to clipboard!
Location
Breda, Netherlands