Position: Machine Learning Engineer (Computer Vision) | MLOps
Category: Data Science
English level: fluent
Years of experience: 4


Have been developing recommendation algorithms at TikTok.

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Before was responsible for the Faces Analytics department and led a team of 5 engineers

As ML part of my job I was responsible for 2 areas of research: Faces Detection & Keypoints Estimation and Faces Attributes recognition where held the whole ML Cycle. Participated in Face Recognition model training and Spoofing identification.

As SE I was responsible for Vide Face Analytic module written in C++. The module took an FHD video stream as input and performed Faces Detection, Tracking, Recognition, Attributes Recognition (Age, Mask, BEard, ...), Spoofing Detection, Re-ID, and Quality estimation for both Faces and Frames. The whole pipeline worked on a few CPU cores in real-time.

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Before
Developed and deployed real-time DL model for Human Body Pose Estimation on a mobile phone.
Developed ML algorithm to imitate personal trainer.

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And Early in my career
Developed  algorithms for Video/Audio segmentation and clustering. Anomalies search in Videos. Query Search in Video and Audio.

------- SKILLS --------

• Languages: : Python, C++ (STL), Bash, JavaScript
• Technologies: : CNN, LSTM, GANs, Transformers, GNNs, Reinforcement Learning
• ML/DL tools: : PyTorch, Lightning, Scikit-Learn, OpenCV, DVC, Hydra, ONNX, OpenVino, W&B, MLflow, TensorBoard
• Data manipulation/visualization tools: : NumPy, Pandas, Matplotlib, Seaborn, NetworkX, Folium
• DevOps: : Docker, Docker-Compose, Git, Gitlab CI/CD, Nginx
• Other: : PyQt, React, HTML, CSS, Unreal Engine, LATEX