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Schedule Interview NowMy name is Ilnar S. and I have over 6 years of experience in the tech industry. I specialize in the following technologies: Python, SQL, Deep Learning, Machine Learning, Automatic Speech Recognition, etc.. I hold a degree in , . Some of the notable projects I’ve worked on include: Tone Models on ProWritingAid.com, A Coqui-STT-based speech recognition system for Kazakh, A 150-lines script to scrape 1200+ Bibles from Bible.com, SDP: A Statistical Dependency Parser Written By Me From Scratch. I am based in Istanbul, Turkey. I've successfully completed 4 projects while developing at Softaims.
I am a dedicated innovator who constantly explores and integrates emerging technologies to give projects a competitive edge. I possess a forward-thinking mindset, always evaluating new tools and methodologies to optimize development workflows and enhance application capabilities. Staying ahead of the curve is my default setting.
At Softaims, I apply this innovative spirit to solve legacy system challenges and build greenfield solutions that define new industry standards. My commitment is to deliver cutting-edge solutions that are both reliable and groundbreaking.
My professional drive is fueled by a desire to automate, optimize, and create highly efficient processes. I thrive in dynamic environments where my ability to quickly master and deploy new skills directly impacts project delivery and client satisfaction.
Main technologies
6 years
1 Year
4 Years
1 Year
Potentially possible
Türk Telekom
My contribution: converted Pytorch models into ONNX, optimized & quantized them on Azure ML and deployed them there.
Fine-tuned a speech-to-text system for Kazakh using the Coqui STT framework. Used Yandex DataSphere's trial 4000 rubles for the GPU. Also have helped to launch Mozilla Common Voice in Kazakh [1] and Tatar [2]. [1] commonvoice.mozilla.org/kk [2] commonvoice.mozilla.org/tt
Bible is the most translated text, and it is often used in NLP research. The referenced script scrapes 1200+ translations of the Bible from bible.com and saves the result in csv files.
A transition-based statistical dependency parser I wrote in Python for a class. The classifier of it is a simple perceptron, but you can plug in any other of your choice. Of course the parser is not as advanced as other parsers you might have used or heard of, but it can serve as a good starting point if you're implementing a dependency parser yourself or just want to see how a transition-based dependency parser works inside.
in German Philology
2006-01-01-2011-01-01
in M.Sc. Computational Linguist
2014-01-01-2017-01-01