Alex Parinov
Moscow, Moscow City, Russia
Summary
🤩
Rockstar🎓
Top SchoolAlex Parinov is a seasoned software engineer and leader with 11 years of experience building production ML systems and 10+ years in software architecture, specializing in deep learning for computer vision, NLP, and time series. He has led cross-functional teams to productionize models and build scalable ML infrastructure spanning cloud and edge deployments for large enterprises such as X5 Retail Group. A core developer of the widely used Albumentations image-augmentation library and a Kaggle Master, he combines open-source impact with hands-on expertise in PyTorch and TensorFlow. Alex’s background spans backend, DevOps, and frontend technologies, enabling him to bridge research, engineering and product needs while optimizing for reliability and customer value. Notably, his contributions to pretrained-models and pytorch-cnn-finetune demonstrate deep practical knowledge of model integration and deployment nuances.
11 years of coding experience
12 years of employment as a software developer
Specialist's degree, Information Technology, Specialist's degree, Information Technology at Samara State Technical University
English, Russian
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (19)
pytorch10
convolutional-neural-networks10
python10
pre-trained-model10
model-driven10
machine-learning10
mask-rcnn10
model-building10
deep-learning10
computer-vision10
faster-rcnn10
image-augmentation10
modeling10
model-driven-development10
image-processing9
Programming languages (6)
Github contributions (5)
creafz/pytorch-cnn-finetune
Feb 2018 - Nov 2019
Fine-tune pretrained Convolutional Neural Networks with PyTorch
Role in this project:
ML Engineer
ML EngineerContributions:72 commits, 3 PRs, 54 pushes in 1 year 8 months
Contributions summary:Alex primarily contributed to the fine-tuning of pre-trained convolutional neural networks using PyTorch. They added support for new models, including NASNet-A Mobile, PNASNet-5-Large, and PolyNet, along with associated tests and wrapper classes. The user also fixed bugs, improved code formatting, and updated the CIFAR-10 example. They also made minor improvements such as bumping the version and setting the input_size for the model in the example.
pytorchresnetefficientnetdeep-learningconvolutional
albumentations-team/albumentations
Jun 2018 - Jun 2022
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
Role in this project:ML Engineer
ML EngineerContributions:6 releases, 84 reviews, 155 commits in 4 years
Contributions summary:Alex focused on refactoring and improving the existing image augmentation library. They reorganized augmentations into separate modules and removed dependencies on external packages like torch and torchvision. They updated and added usage examples in the notebook, and renamed the iaa module to imgaug. The user also made parameters configurable and updated function comments, which implies effort on maintainability and usability of the library.
fast-augmentationspythonalbumentationsdetectiondeep-learning
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