Prakash Sellathurai
Software Engineer at Amazon
Bengaluru, Karnataka, India
Summary
👤
Senior🎓
Top SchoolPrakash Sellathurai is a software engineer with nine years of experience building scalable backend and AI-driven systems, most recently delivering security and user-experience features for Alexa Communications at Amazon. He has a strong machine-learning and computer-vision background from Bigthinx, where he cut image-processing latency by 60% and deployed GPU-accelerated, edge-friendly solutions. Prakash contributes to open-source ML tooling—adding robust dataset-splitting utilities and tests to the widely used Keras repository—demonstrating attention to correctness and diverse input handling. He combines practical systems engineering (C++, Docker, parallelism) with product-focused experimentation, such as A/B-tested setup flows, and holds a BE in Mechatronics and Robotics. Notably, he’s delivered both low-level performance optimizations and high-impact security features like malware scanning within LLM architectures.
8 years of coding experience
3 years of employment as a software developer
Bachelor of Engineering, Mechatronics, Robotics, and Automation Engineering, CGPA: 8.46, Bachelor of Engineering, Mechatronics, Robotics, and Automation Engineering, CGPA: 8.46 at Sri Krishna College of Engineering and Technology
English, Tamil
Stackoverflow
107reputation
3kreached
6answers
2questions
Github Skills (12)
machine-learning10
deeplearning-ai10
deep-learning10
tensorflow10
python10
data-science9
numpy9
substring6
neural-network6
algorithm6
interpolation6
image-processing6
Programming languages (16)
Github contributions (5)
keras-team/keras
Apr 2022 - May 2022
Deep Learning for humans
Role in this project:
Back-end Developer
Back-end DeveloperContributions:4 reviews, 24 commits, 1 PR in 21 days
Contributions summary:Prakash primarily contributed to the development of the `keras.utils.split_dataset` utility, adding functionality to split datasets for training and validation. Their work involved implementing the core logic of the function, incorporating features such as shuffling and size control. They also addressed bugs and made improvements to handle different dataset input types. The user further enhanced the utility by adding test cases and making it more robust.
pythondata-sciencedeep-learningneural-networksmachine-learning
prakashsellathurai/a-grim-loth
Jun 2021 - Jul 2022
Essential Algorithms / Data structures implemented in c++ and python
Contributions:2 releases, 46 reviews, 309 commits in 1 year 1 month
cpppythondatastructuresalgorithms-and-data-structuresessential
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