Indu Thangakrishnan
Software Development Engineer at Amazon Web Services
Sunnyvale, California, United States
Summary
🤩
Rockstar🎓
Top SchoolIndu Thangakrishnan is a Software Development Engineer with nine years of experience specializing in distributed deep learning training and infrastructure at AWS, currently focused on Trainium-based workloads. She has a strong background in optimizing collective communication for scalable model training and brings systems-level experience from earlier work in network security, Windows internals, mobile apps, and parsers. Her open-source contributions include documentation and compatibility improvements to high-profile MXNet projects, helping make deep learning tooling more accessible and reliable. Based in Sunnyvale, she pairs low-level systems expertise with practical ML infrastructure know-how, enabling performant production deployments. Colleagues describe her as a pragmatic engineer who moves between research-grade performance optimization and clear, user-focused documentation.
9 years of coding experience
8 years of employment as a software developer
Bachelor of Engineering - BE, Computer Science, Bachelor of Engineering - BE, Computer Science at Anna University Chennai
Github Skills (11)
mxnet10
jupyter-notebook10
gluon10
python10
documentation10
deep-learning9
api-documentation9
data-serialization8
serialization8
trainings7
modeling7
Programming languages (8)
Github contributions (5)
apache/mxnet
Apr 2017 - Aug 2018
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Role in this project:
Technical Writer
Technical WriterContributions:38 commits, 126 PRs, 72 pushes in 1 year 4 months
Contributions summary:Indu primarily focused on updating and improving the documentation within the `apache/mxnet` repository. Their commits involved modifying documentation for various functions and features, including `ndarray` functions, `log_softmax`, `Custom operator`, and initializers. They also corrected formatting and clarified usage instructions, demonstrating a focus on enhancing the clarity and accuracy of the project's documentation for users.
pythonschedulerdataflowmutationdata-science
zackchase/mxnet-the-straight-dope
Jun 2018 - Jul 2018
An interactive book on deep learning. Much easy, so MXNet. Wow. [Straight Dope is growing up] ---> Much of this content has been incorporated into the new Dive into Deep Learning Book available at https://d2l.ai/.
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:5 commits, 4 PRs in 20 days
Contributions summary:Indu primarily focused on improving the project's compatibility and functionality, addressing Python 2-related issues and adapting the code for the testing environment. They also made changes to the notebook configurations, utilizing GPU resources when available and updating parameter saving and loading methods. Moreover, the user cleaned up the notebooks by removing output cells, demonstrating a focus on code maintainability and improving user experience.
pytorchd2lmxnetdeep-learninghas-content
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