Ishan Rai
Software Development Engineer II at Amazon
Berlin, Germany
Summary
👤
Senior🎓
Top SchoolIshan Rai is a software engineer with eight years of experience building ML-driven systems and web platforms, currently working at Amazon on machine translation and LLM hosting from Berlin. An IIT Roorkee graduate, he has delivered production translation pipelines for both asynchronous localization and synchronous search applications, and previously modernized risk and blockchain tooling at JPMorgan Chase. Ishan is an active open-source contributor with hands-on experience in deep learning (Chainer/ChainerX) and distributed storage/front-end UX (Ceph), where he improved test quality, UI consistency, and performance profiling. He brings a pragmatic blend of backend, frontend, and ML engineering—comfortable moving between C++, Python, React, and cloud-native deployments—and has a track record of adding testing, CI/CD, and monitoring automation from CERN to Amazon. Notably, his open-source work emphasizes maintainability and observability, such as refactoring test suites and implementing profiling tools that reveal hidden inefficiencies.
8 years of coding experience
3 years of employment as a software developer
Indian Institute of Technology Roorkee
German, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (33)
unit-testing10
debugging10
debug10
dashboard10
python10
multithreading10
chainer10
testing10
css10
numpy10
distributed-computing10
thread10
hyper-threading10
profiling10
html10
Programming languages (10)
Github contributions (5)
ganga-devs/ganga
May 2019 - May 2020
Ganga is an easy-to-use frontend for job definition and management
Role in this project:
Backend Developer
Backend DeveloperContributions:42 commits, 12 PRs, 33 pushes in 1 year
Contributions summary:Ishan primarily contributed to the backend of the Ganga project, focusing on bug fixes and feature additions. Their work involved correcting typos, merging code from the 'develop' branch, and implementing profiling tools for memory and CPU usage. The user also addressed deprecation warnings and improved thread management within the system, specifically optimizing worker threads and the shutdown process.
golanguser-interfacecernlhcbdistributed-computing
chainer/chainer
Feb 2019 - Sep 2019
A flexible framework of neural networks for deep learning
Role in this project:QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:65 commits, 48 PRs, 63 comments in 7 months
Contributions summary:Ishan's commits primarily involve simplifying and refactoring existing test cases within the Chainer deep learning framework. They focused on converting existing test implementations to use the `testing.FunctionTestCase` class, reducing code duplication and improving test structure. The user addressed multiple test files, including those for activation functions, array manipulations, and pooling operations. Their work aimed to improve the overall quality and maintainability of the Chainer testing suite.
cudapythonmxnetcaffe2flexible-framework
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