AI Engineer Intern · OeNB
Built and evaluated document-grounded AI workflows spanning preprocessing, retrieval, language-model experiments, and retrieval evaluation. The internship was extended from six to nine months following strong performance.
Engineering AI that can be inspected, evaluated, and trusted.
I am an Applied AI engineer in Vienna, focused on retrieval systems, language models, machine learning, and the experiments that make their behavior understandable.
My work moves between system building and research: processing real documents, evaluating retrieval, comparing sequence architectures, and turning difficult model behavior into clear evidence.
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Experience
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Education
Master's in Artificial Intelligence
Johannes Kepler University Linz
Bachelor's in Artificial Intelligence
Johannes Kepler University Linz
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Working method
Make the boundary visible.
State what the system can support, what the evidence cannot prove, and where a reconstruction begins.
Evaluate the right failure.
Choose metrics that expose retrieval quality, class imbalance, uncertainty, or capacity rather than hiding them.
Explain through interaction.
Let people inspect a result directly when a static score would flatten the most important behavior.
Actively looking for Applied AI and LLM opportunities in Vienna or remote across the EU.