Jay Nanavati
Principal Technologist at AVEVA
Hyderabad, Telangana, India
Summary
👤
Senior🎓
Top SchoolJay Nanavati is a Principal Technologist with 12+ years of hands-on experience building desktop, web, mobile and cloud applications, currently driving line-of-business solutions for the Oil & Gas domain at AVEVA. He combines deep Microsoft-stack expertise (C#, WPF, MVVM, Blazor, Xamarin) with modern cloud and DevOps practices across Azure and AWS, and has authored multiple technical books on Silverlight and Windows app patterns. Early freelance work and a stint at Microsoft shaped his pragmatic, customer-focused engineering style, and he continues to ship production code via UpWork to stay connected to real-world problems. An avid coder who started with VB6, he also contributes to open-source ML projects—improving RNN calibration and padding in a well-regarded medical imaging repo—showing curiosity beyond traditional enterprise domains. Jay’s blend of authorship, enterprise product development, and occasional freelance hustle makes him a rare mix of architect, implementer, and communicator.
12 years of coding experience
13 years of employment as a software developer
BCA, Computer applications, IT, BCA, Computer applications, IT at Saurashtra University
Stackoverflow
1,109reputation
106kreached
8answers
41questions
Github Skills (17)
pytorch10
python10
machine-learning10
rnn-model10
n10
deep-learning10
medical-imaging10
health9
microsoft-azure9
ehealth9
medical9
azure9
csv6
wpf6
reporting-services6
Programming languages (9)
Github contributions (5)
microsoft/InnerEye-DeepLearning
Aug 2020 - Oct 2020
Medical Imaging Deep Learning library to train and deploy 3D segmentation models on Azure Machine Learning
Role in this project:
ML Engineer
ML EngineerContributions:24 reviews, 12 commits, 10 PRs in 2 months
Contributions summary:Jay primarily focused on enhancing the RNN model within the medical imaging deep learning library. They implemented padding for RNN outputs to ensure correct target indices. Additionally, the user added temperature scaling capabilities to sequence models, improving the model's calibration and reliability. These changes directly impact the model's performance and the overall training process.
deep-learningmachine-learningazure-machine-learningdeep-learning-libraryimaging
nvorkinn/facebookCopy
Jan 2014 - Mar 2014
Contributions:63 commits in 1 month
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