Ritesh Maheshwari
Interim Chief Technology Officer at Authentic Memory
Sunnyvale, California, United States
Summary
🤩
Rockstar🎓
Top SchoolRitesh Maheshwari is a Senior Engineering Manager with 13 years of experience leading large-scale performance and media platform efforts at LinkedIn from Sunnyvale. He currently runs the Media Experience org, improving video time-to-first-frame by 60–70% on mobile, raising upload reliability, and deploying auto-captions—work that blends client and server optimizations across VOD, live, images, docs and VC. His background in network and site-speed engineering (including TCP, Brotli, RUM and anomaly detection efforts) and prior role at Akamai give him deep expertise in transport, CDN and end-to-end performance. Ritesh pairs data-driven instrumentation and A/B testing with product sensibility to move key engagement metrics, and he’s known for turning complex measurement systems into actionable optimizations. An early researcher in wireless systems with graduate work at Stony Brook and an IIT Kharagpur alumnus, he brings both academic rigor and production-hardened execution to platform engineering.
13 years of coding experience
17 years of employment as a software developer
Bachelor’s Degree, Computer Science & Engineering, Bachelor’s Degree, Computer Science & Engineering at Indian Institute of Technology, KharagpurDoctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at Stony Brook University
Stackoverflow
556reputation
69kreached
9answers
3questions
Github Skills (35)
anomaly-detection10
r9
java9
ensemble-learning9
pca9
svm9
time-series9
naive-bayes9
random-forest9
python8
h2o8
kmeans8
elastic-net8
gpu8
machine-learning8
Programming languages (3)
Github contributions (5)
linkedin/luminol
Nov 2015 - Jan 2018
Anomaly Detection and Correlation library
Contributions:16 commits, 12 PRs, 11 pushes in 2 years 2 months
anomaly-detectionanomalydetection
LinkedInAttic/naarad
Feb 2013 - Jan 2016
Naarad is a highly configurable system analysis tool that parses and plots timeseries data for better visual correlation. Naarad was built to help in performance analysis and investigations.
Contributions:339 commits, 16 PRs, 14 pushes in 2 years 11 months
time-series
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