Position: Machine Learning Engineer
Category: Data Science
Experience: 4 years | English: upper

- Developed and trained video classification and object detection models for 350+ classes at Samba TV, reducing the neural network size by 30% and optimizing architecture using quantization and distillation.
- Increased accuracy of image classification model by 40% at Reface, achieving a significant improvement in the accuracy of its image classification model, which could have had a positive impact on the company's product offerings.
- Developed and launched a personal blog about artificial intelligence, sharing insights and experiences on a variety of ML and DL topics.
- Participated in hackathons and competitions, developing innovative solutions and earning accolades such as first place among 16 teams in Reface Wild Wild Hack 2021 and 55% accuracy in Google Hash Code 2019.
- As a Machine Learning Engineer at Samba TV, trained video classification and object detection models for 350+ classes, reduced the neural network size by 30%, and optimized architecture using quantization and distillation. Developed horizontally scaled data pipelines with AWS services, accelerating data processing, and conducted research for insights on new model optimization methods and edge-device architectures.
    
- As a Machine Learning Researcher at Reface, increased the accuracy of the image classification model by 40% compared to the baseline, managed datasets to help machine learning engineers find data quickly, and conducted research for new trending machine learning applications.
    
- As a Teacher of Machine Learning at Hillel IT School, taught a range of topics, including mathematics for ML, regression, dimensionality reduction, clustering, decision trees, as well as ML concepts such as convolutional, recurrent, transformer layers, and generative models. Also taught a wide range of Deep Learning areas, such as Computer Vision, NLP, and Recommendation Systems.
    
- As an Artificial Intelligence Developer in freelance work, solved clients' problems with Deep Learning and Machine Learning methods, using a range of technologies including PyTorch, TensorFlow, Keras, scikit-learn, Docker, AWS, Databricks, MLflow, OpenCV, DVC, Lighting, and Streamlit.
    
- Holds a Master of Mathematics and Computer Science and a Bachelor of Mathematics and Computer Science from Taras Shevchenko National University of Kyiv.
    
- Worked on several projects, including a Medium blog about Artificial Intelligence, a personal blog about machine learning experience, a Telegram channel about the latest machine learning news and publications, and a tracker of the latest machine learning publications and code.
    
- Interested in working on cutting-edge AI research and development projects related to computer vision, NLP, and recommendation systems. Also interested in exploring new AI technologies and techniques and sharing knowledge through teaching and mentoring.
As a machine learning engineer with a strong background in Deep Learning, I have worked on a variety of projects involving media such as images and videos. Given my expertise and interests, I am particularly drawn to working on projects that involve media data and would like to focus on Deep Learning in this domain moving forward: Computer Vision, NLP, or Audio (speech). I am not interested in working with tabular data and would like to pursue opportunities that align with my interests and strengths.