Sujay Dey is an Associate QA Engineer based in Kolkata with four years of experience blending quality assurance, backend development, and open-source contribution. At AccuKnox he progressed from Solution Engineer and QA Trainee to his current QA role, bringing practical product-facing experience to testing and validation. He has contributed backend schema and validation work to the widely used Oppia learning platform and helped improve documentation and configurations for Hugging Face Transformers models, reflecting both systems-thinking and ML-awareness. Comfortable across cloud-native and Kubernetes environments, Sujay pairs hands-on coding skills with a tester’s attention to detail. He’s a lifelong learner who actively bridges open-source collaboration and enterprise QA practices to deliver reliable, production-ready software.
4 years of coding experience
Bachelor's degree, ENGINEERING, Bachelor's degree, ENGINEERING at Narula Institute Of Technology
A free, online learning platform to make quality education accessible for all.
Role in this project:
Back-end Developer
Contributions:366 reviews, 19 commits, 59 PRs in 8 months
Contributions summary:Sujay primarily contributed to the back-end of the Oppia platform by adding argument schemas for various handlers and updating existing ones. The user's work included adding schemas for the `DeleteAccountHandler`, `LearnerIncompleteActivityHandler`, `RatingHandler`, and `FlagExplorationHandler` classes. They also added argument schemas for the `ExplorationCompleteEventHandler`, `ExplorationMaybeLeaveHandler`, and `SolutionHitEventHandler` classes. These changes involved modifying several core controllers and ensuring proper data validation within the system.
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Role in this project:
ML Engineer
Contributions:14 reviews, 5 commits, 5 PRs in 2 months
Contributions summary:Sujay primarily contributed to documentation and configuration updates within the Hugging Face Transformers repository, focusing on doctests for various models. They added configuration examples for `trajectory_transformer`, `vision_text_dual_encoder`, `vision_encoder_decoder`, and `time_series_transformer` models. A significant contribution involved removing a dependency from the `mT5` model.
pythonbertspeech-recognitionstate-of-the-artflax
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