Make the system legible
Engineering quality emerges from clarity. I optimize for readable code, readable interfaces, readable decisions.
Felix Bergmann · Portfolio 01
I lead testing and quality assurance for AI products at TeamViewer, study AI at JKU Linz, and co-founded Effizienzwerk to build custom AI applications.

I’m a Bachelor’s student of Artificial Intelligence at Johannes Kepler University, focused on applied machine learning, neural networks, and the systems that put them in front of real users.
At TeamViewer I work as an AI engineer spearheading testing and quality assurance for AI products. I’m building that foundation from the ground up: evaluation workflows, automation, reproducible checks, and the release discipline needed for AI systems people can trust.
I also co-founded Effizienzwerk, where we build effizienzwerkMail, one of our leading AI email productivity suites, and effizienzwerkVoice for voice-first productivity. That means moving from product discovery to implementation, connecting LLMs, agents, automation, and software engineering into something that is actually usable.
Outside engineering, I lead the percussion section of my hometown wind band, co-founded Austria’s first drumline, and support neuron.ai — Austria’s first student-led AI initiative.
— Operating principles
Engineering quality emerges from clarity. I optimize for readable code, readable interfaces, readable decisions.
Models, abstractions and animations cost focus. I only add them when they pay back in user understanding or product reliability.
Production is the lab. A measured release teaches more than another month of speculation.
A path through mathematical foundations, applied machine learning, and large-scale computing. Bachelor thesis in collaboration with the Medical University of Vienna.
Degree
Johannes Kepler University Linz · 2023 — 2026
The JKU AI programme combines deep mathematical foundations, modern machine-learning theory, and applied projects across language, vision, and life sciences. Current GPA: 1.1 on the Austrian scale, equivalent to 3.9 in the US system.
Austria · 3.9 US
Incoming master
Specialization in Machine Learning and Large Scale Computing, with the first year at KTH and the second year at Aalto University.
Stockholm, Sweden
Data Science foundations, systems, and applied ML.
Espoo, Finland
Specialization in Machine Learning and Large Scale Computing.
Bachelor thesis · with Medical University of Vienna
A registration pipeline for retinal imaging that aligns multi-session fundus images of the same eye for longitudinal lesion tracking — built to stay stable under changes in illumination, scale, and pathology.
Selected coursework
Mathematical language & foundations
JKU entryThe first mathematical backbone course for AI: precise notation, proof strategies, logic, sets, functions, matrices, linear systems, sequences, and series.
Focused projects across deep learning, ML systems, and product UI. Click any card to read the case study.

Implementation of the famous Transformer architecture from the groundbreaking paper

Comprehensive machine learning projects including customer churn prediction and recommendation systems

Achieved top-3 ranking in image classification competition with 400+ participants

Large Language Model fine-tuning project using advanced techniques like LoRA and PEFT
A short editorial feed of recent project releases, write-ups, and milestones.

Documented implementation details and benchmarks for my Transformer build — attention mechanics, scaling, and comparisons.
Read
Top-3 ranking out of 400+ participants with modern augmentations and an EfficientNet/ResNet ensemble.
Read
Completed LLM fine-tuning with LoRA/PEFT on SageMaker; wrote deployment notes and an evaluation playbook.
ReadA working list of the certifications and short courses I’ve completed — focused on AI/ML, language models, and the surrounding software craft.
2024
AWS
2024
DeepLearning.AI
Jun 2024
DeepLearning.AI
Jun 2024
DeepLearning.AI
Jun 2024
Cisco Networking Academy
Feb 2023
From IT support to AI product QA and startup delivery — a measured progression through systems that need to work under real constraints.
neuron.ai connects AI students and practitioners across Austrian universities through paper readings, expert talks, and public debates.
in Linz, Austria
Brainery · Debates
across universities
A student-led reading group exploring seminal and current ML/AI papers together — small group, deep discussion.
Topics
Invited talks from researchers and industry practitioners — Weights & Biases, Stanford alumni, and AI engineers.
Topics
Public discussion evenings on sustainability, scaling, and the cultural impact of AI.
Topics
“Bridging research and practice — empowering students to become thoughtful, impactful AI practitioners in Austria.”
Music keeps my engineering honest. Tempo, dynamics, listening — the same disciplines, in different keys.
Instruments
Marching & concert percussion
Classical & pop
Alpine folk standards
Groove fundamentals
Bands & projects
Wind Band · Concert & Marching
I lead the percussion section, run rehearsals, and perform at concerts, parades, and community events.
Reach me about applied-AI collaborations, custom AI products, AI quality systems, or to talk about the work — coffee, code, or both.
The quickest way to reach me is the form. For longer conversations and project briefs, email or LinkedIn work just as well.
Currently based in Salzburg & Linz, Austria.