Anirudh Ramanan
Senior Architect at super.money
Bengaluru, Karnataka, India
Summary
🤩
Rockstar🎓
Top SchoolAnirudh Ramanan is a Senior Architect with a decade of experience designing high-performance frontend and backend systems, currently shaping mobile and platform architecture at super.money. He has led product and infrastructure work across startups and large enterprise, from Flipkart’s video player and React Native frameworks to NextLeap’s AI-driven learning products like an AI mock interview platform and code-assist bots. Known for shipping practical performance optimizations—app size and boot-time reductions, JSON parsing speedups, and profiling-led improvements—he also authors open-source tooling that powers video ads and developer productivity. He combines hands-on engineering with product thinking, having built payments, compilers, video conferencing, and internal dashboards that reduced operational overhead. Trained at Georgia Tech, he blends academic rigor with a tinkerer’s curiosity evident in his developer-first projects and libraries.
10 years of coding experience
9 years of employment as a software developer
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at Georgia Institute of TechnologyNanodegree, Nanodegree at Udacity
English
Stackoverflow
706reputation
703kreached
29answers
0questions
firebase
top-5%
firebase-cloud-messaging
top-5%
Github Skills (144)
uipageviewcontroller10
json-parser10
webview10
batching10
exponential10
operation10
network-analysis10
ads-manager10
render10
deserialization10
persistence10
fabric10
ima10
swipeview10
vast10
Programming languages (11)
Github contributions (5)
anirudhramanan/plutus-algo-backtesting
Jan 2019 - Sep 2022
A python framework to run backtest on stocks using your own custom algorithmic strategies
Contributions:23 commits, 12 pushes, 1 branch in 3 years 8 months
stock-marketpythonalgorithmic-tradingtradingbacktest
flipkart-incubator/batchman
Mar 2016 - Jan 2020
This library for Android will take any set of events and batch them up before sending it to the server. It also supports persisting the events on disk so that no event gets lost because of an app crash. Typically used for developing any in-house analytics sdk where you have to make a single api call to push events to the server but you want to optimize the calls so that the api call happens only once per x events, or say once per x minutes. It also supports exponential backoff in case of network failures
Contributions:19 releases, 2 reviews, 199 commits in 3 years 11 months
operationexponential-backoffpushdevelopingbatch-processing
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