Anubhav Singh
Software Engineer BA4 at Barclays
Pune, Maharashtra, India
Summary
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Rockstar🎓
Top SchoolAnubhav Singh is a software engineer with 8 years of experience building enterprise-grade applications and REST APIs, currently working at Barclays after senior roles at Infosys. He specializes in Node.js/Express and SAP HANA (XSJS Classic and Advanced), and is comfortable across JavaScript, Python, Java, and C/C++ while leveraging cloud-native tooling like AWS, Docker, Kubernetes and Git. An AWS Certified Developer with Node and Python certifications, he combines strong backend engineering with performance-focused work in privacy-preserving ML—contributing benchmarking and stability fixes to the well-known OpenMined/PySyft project. His background in cloud computing (BTech with a 9.32 GPA) and hands-on cryptographic SMPC development gives him a pragmatic edge when architecting secure, scalable data collaborations.
8 years of coding experience
5 years of employment as a software developer
Bachelor of Technology (BTech), CSE with Specialization in Cloud Computing and Virtulaization, CGPA : 9.32 / 10.0, Bachelor of Technology (BTech), CSE with Specialization in Cloud Computing and Virtulaization, CGPA : 9.32 / 10.0 at Galgotias University
English, Hindi
Stackoverflow
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Github Skills (14)
pytorch10
deeplearning-ai10
deep-learning10
federated-learning10
python10
sym10
cryptography9
machine-learning9
benchmarking9
benchmark9
secure-computation9
data-science7
testing7
numpy7
Programming languages (8)
Github contributions (5)
OpenMined/PySyft
Aug 2020 - Sep 2021
Perform data science on data that remains in someone else's server
Role in this project:
ML Engineer / Data Scientist
ML Engineer / Data ScientistContributions:47 reviews, 19 commits, 22 PRs in 1 year 1 month
Contributions summary:Anubhav focused on benchmarking and improving the performance of the PySyft library, particularly around privacy-preserving machine learning techniques. Their contributions involved implementing scripts and graphs for benchmarking various approximation methods like sigmoid functions, max/avg pool2d, and AST operations. They also addressed and fixed issues related to data types and comparisons within the framework, contributing to the stability and functionality of the library's core features.
pytorchcryptographyacquiringpythonscience
aanurraj/sympc
Nov 2020 - Jul 2021
Contributions:2 PRs, 156 pushes, 39 branches in 8 months
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