Open to opportunities

Paweł Piwowarski

@pawepiwowarski

Message

Graduate AI & Data Science specialist focused on computer vision and LLM applications.

Spain

Message

What I'm looking for

I’m seeking full-time roles applying computer vision and LLMs where I can build and deploy production ML systems, collaborate with interdisciplinary teams, and continue research-driven impact.

I am a graduate in AI and Data Science with strong expertise in computer vision, LLMs, and practical MLOps, backed by hands-on projects like an awarded Master’s thesis on aerial photography localization and a commissioned museum recommendation system. I combine deep technical skills in PyTorch, Hugging Face, and full‑stack deployment with research rigor and collaborative experience from academic and industry-aligned projects.

I have taught university courses and served as a teaching assistant, sharpening my communication, leadership, and mentoring capabilities while delivering production-ready ML systems. I seek roles where I can apply advanced machine learning, drive model deployment at scale, and contribute to impactful, real-world AI solutions.

Experience

Work history, roles, and key accomplishments

MSc Thesis Researcher

Universiteit van Amsterdam

Jan 2025 - Jul 2025 (6 months)

Developed a PyTorch and Vision Transformer-based aerial imagery localization system, fine-tuned foundation models (DINOv2), and deployed the model via Hugging Face API; thesis awarded 9/10.

PyTorch Vision Transformer DinoV2 Hugging Face Fine Tuning Computer Vision MLOps

Teaching Assistant

Vrije Universiteit Amsterdam

Apr 2023 - Jul 2025 (2 years 3 months)

Led exercise sessions, graded assignments and exams, and provided detailed feedback for courses in Databases, Machine Learning, and Computational Intelligence, improving student understanding and assessment quality.

Teaching Grading Machine Learning Databases Communication Presentation

Full-Stack ML Project Lead

Museum.nl

Oct 2024 - Feb 2025 (4 months)

Designed and implemented a full-stack museum recommendation system (Next.js frontend, Python ML backend, PostgreSQL) using collaborative and content-based algorithms to personalize recommendations for 500+ museums' users.