Gyanesh Malhotra
Software Engineer 3 at Zepto
Bengaluru, Karnataka, India
Summary
👤
Senior🎓
Top SchoolGyanesh Malhotra is a seasoned software engineer with 9 years of experience building scalable, production-grade services using domain-driven design, automated deployments, and robust testing and monitoring. He has shipped core backend features across fintech and e‑commerce stacks at Razorpay and Zepto, working on lending, corporate cards, collections and high-throughput services. Comfortable across the stack, he blends API design, data-loading reliability and test-driven development—evidenced by backend contributions to the widely used razorpay/ifsc repository adding validation and sublet support. His background spans applied ML for time-series forecasting to systems migrations and visual odometry research, reflecting a strong appetite for both practical engineering and algorithmic problem solving. Based in Bengaluru, he brings pragmatic engineering discipline and a track record of turning complex business flows into resilient, observable systems.
9 years of coding experience
7 years of employment as a software developer
Bachelor of Engineering & Msc Computer Science & Economics, Bachelor of Engineering & Msc Computer Science & Economics at Birla Institute of Technology and Science, Pilani - Goa Campus
English, Hindi
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (12)
api10
go10
apidoc10
testing9
go-testing9
json9
data-structures8
data-structure8
alembic6
sqlalchemy6
python6
flask6
Programming languages (8)
Github contributions (5)
razorpay/ifsc
Feb 2020 - Dec 2022
:bank: IFSC Codes Repository
Role in this project:
Back-end Developer
Back-end DeveloperContributions:14 reviews, 22 commits, 1 PR in 2 years 10 months
Contributions summary:Gyanesh's primary contributions involved modifications to the `ifsc` repository, focusing on backend logic. They introduced validation and sublet support, along with custom sublet features. Code changes included modifications to `Bank.go`, `IFSC.go`, and `client.go` and `constants.go`, indicating work on data loading, and API interactions. The user also made adjustments to the `go.mod` file and test files, showing responsibilities that span across core functionality and testing.
bankifscifsc-codes
gyanesh-m/Monocular-visual-odometry
May 2018 - Dec 2019
GSOC-2K18: This project uses dense optical flow based on FlowNet2 model, to estimate the VO trajectory.
Contributions:22 commits, 1 push, 1 branch in 1 year 7 months
visual-odometrydeep-learningoptical-flowgsoccomputer-vision
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