Sreegandh Devireddy
Mid Level Software Engineer at Performativ - Wealth Management Software
United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolSreegandh Devireddy is a mid-level software engineer based in the UK with five years of experience building product-focused web and backend systems across startups and freelance engagements. He has served as a founding engineer at Unify and currently contributes to wealth management software at Performativ, bringing a blend of product ownership and hands-on engineering. His open-source backend work on the ivy project shows a knack for making ML code portable across frameworks, including implementing Array API-compliant functions and refactors that improve cross-framework compatibility. Comfortable across the stack, he pairs a Computer Science with AI background from the University of Leeds with practical startup experience, often stepping into product roles beyond pure coding. Colleagues would describe him as pragmatic and curious, equally at home shipping features and improving core libraries that help other developers.
4 years of coding experience
3 years of employment as a software developer
ISC(Indian Standard XII), Science, ISC(Indian Standard XII), Science at Bishop Cotton Boys' SchoolBachelor's degree, Computer Science with Artificial Intelligence, Bachelor's degree, Computer Science with Artificial Intelligence at University of Leeds
English, Telugu, Hindi, Kannada
Github Skills (10)
pytorch10
machine-learning10
api10
jax10
tensorflow10
python10
numpy10
converter9
deeplearning-ai9
deep-learning9
Programming languages (3)
Github contributions (5)
ivy-llc/ivy
Mar 2022 - Dec 2022
Convert Machine Learning Code Between Frameworks
Role in this project:
Back-end Developer
Back-end DeveloperContributions:5 reviews, 48 commits, 60 PRs in 8 months
Contributions summary:Sreegandh primarily contributed to implementing and refactoring core functionalities within the `ivy` project, focusing on converting machine learning code between various frameworks. Their commits demonstrate the addition of new features such as the `logical_xor` function, adhering to the Array API standard. They also refactored and improved existing functions like `to_numpy`, `to_scalar`, and `to_list` in the backend implementations of multiple frameworks, enhancing the project's cross-framework compatibility. These updates were crucial in improving the usability and functionality of the code conversion tools.
pythontensorflowframework-learningtemplatedata-science
sreegandh804/ivy
Aug 2022 - Feb 2024
The Unified Machine Learning Framework
Contributions:12 pushes in 1 year 5 months
pythoncaffe2data-sciencedeep-learningmachine-learning
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