Raghavendra Kotikalapudi
Member Of Technical Staff at Microsoft AI
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolRaghavendra Kotikalapudi is a Member of Technical Staff with a decade of experience building and optimizing large-scale ML and LLM systems from research to production. He has driven post-training and RL research on high-impact LLM projects at Google DeepMind (core contributor to Gemini advancements) and now continues that work on Microsoft Super Intelligence. Earlier roles at Google and Amazon focused on applied ML for ads, pricing and video understanding, giving him a strong track record of productizing research and shipping robust pipelines. An active practitioner of deep learning engineering, he contributed an open-source Keras ResNet implementation and CIFAR10 examples that reflect hands-on model design and reproducible experimentation. Based in Mountain View, he blends academic rigor from an MS in Computer Science with practical system-building across cloud-scale teams. Colleagues value him for tackling hard reasoning and safety problems in LLMs while keeping an eye on real-world impact.
10 years of coding experience
16 years of employment as a software developer
Master’s Degree Computer Science, Master’s Degree Computer Science at Missouri University of Science and TechnologyBachelor’s Degree Computer Science, Bachelor’s Degree Computer Science at Shri Mata Vaishno Devi University
Github Skills (13)
neural-network10
keras10
machine-learning10
convolutional-neural-networks10
deep-learning10
resnet10
python10
image-classification10
modeling9
compile9
trainings9
tensorflow9
compilation9
Programming languages (4)
Github contributions (5)
raghakot/keras-resnet
Apr 2016 - Apr 2017
Residual networks implementation using Keras-1.0 functional API
Role in this project:
ML Engineer
ML EngineerContributions:30 commits, 7 PRs, 33 pushes in 1 year
Contributions summary:Raghavendra primarily contributed to the implementation of a ResNet model using Keras, focusing on the architecture definition and ensuring compatibility with different image dimension orderings. They addressed bugs related to the model's functionality and made improvements to the code. The user also integrated a CIFAR10 training example, showcasing practical usage of the implemented ResNet architecture.
apiresidual-networksdeep-learningtheanoneural-networks
raghakot/ultrasound-nerve-segmentation
Aug 2016 - Jan 2017
Contributions:12 commits, 11 pushes, 1 branch in 5 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.