Abdulkader Abdulrazzak
Artificial Intelligence Engineer & Data Scientist based in Vienna.
I grew up in Aleppo, Syria. I studied informatics at IMC Krems and did my master's in AI at JKU Linz. I'm currently based in Vienna, Austria. I arrived in Austria as a refugee in 2015, and I worked my way from unskilled jobs, such as a security guard and warehouse worker, to data science and AI. My focus has been on efficient fine-tuning techniques, memory optimization, and building data-driven systems that leverage LLMs to reduce workload. Beyond AI, I'm interested in finance, and passionate about philosophy. I'm also a 19th century history nerd. To wind down, I often play strategy games.
I taught myself to code during night shifts as a security guard, completed internships at Microsoft and STRABAG, and recently finished my Master's focusing on deep learning and applied AI.
Selected Impact:
- Light for the World: Built a specialized AI harness to automate the generation of the strategy evaluation, reducing workload from months to days and slashing external consulting costs. Built a system to automate data workflows that were otherwise performed manually, reducing workload from days to minutes.
- Baubot: Developed robotic data-collection suites for model training which successfully solved a critical problem in robot positioning. Improved client-facing 3D simulation software by reworking its algorithms, which boosted performance significantly. Implemented features in direct contact with clients.
Iām currently looking for full-time Machine Learning Engineer / Data Scientist opportunities.
For a complete overview of my experience, education, and technical background, please view my resume →.
Current topics, research areas, and questions I am exploring:
Parameter-efficient fine-tuning is one of the most creative subdomains of deep learning, and it's immensely valuable in the industry. I keep myself informed on the latest research and techniques.
The questions on consciousness are forever pervasive to the human discourse. Lately, there has been some research on AI consciousness which I find provocative and intriguing.
I'm always down to talk about finance, markets, and portfolio management.
My favorite subdomain in AI. Among all paradigms, I find Reinforcement learning the most profound. It's the most similar to how we learn as humans, which is why I tend to romanticize it a bit.
Six books I've read and recommend in no particular order:
Often called the bible of reinforcement learning. It's a dense book written by the pioneers of the field. Finishing it felt like a great milestone, and in my opinion, reinforcement learning in general has some of the most beautiful mathematics in the field. I keep going back to it and I highly recommend it to everyone in the field.