Tejas Khanolkar
Sunnyvale, California, United States
Summary
🤩
Rockstar🎓
Top SchoolTejas Khanolkar is a software engineer with seven years of experience building full-stack web applications and production APIs, currently contributing to AWS Amplify after hands-on roles at WeightWatchers and Purplle. He combines practical backend expertise (Node.js, NestJS, MongoDB, Kafka) with front-end work in AngularJS/React and a research-driven focus on deep learning, computer vision, and NLP from his MS at USC. Tejas has moved ML prototypes into applied domains—predictive refinery modeling, automated lung disease diagnosis, and fake-profile detection—and co-authored two peer-reviewed papers. Notable production impact includes reducing redundant food entries by 60% at WeightWatchers and implementing enum handling and SLO tracking at AWS Amplify that improved reliability and saved costs. Based in Sunnyvale, he pairs a research mindset with pragmatic engineering to solve real-world problems and enjoys cricket and podcasts outside work.
7 years of coding experience
2 years of employment as a software developer
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at University of Southern CaliforniaBachelor of Technology - BTech, Computer Science, Bachelor of Technology - BTech, Computer Science at KJ Somaiya College of Engineering, Vidyavihar
Github Skills (43)
aws-appsync10
lambda-functions10
aws-fargate10
storage10
aws-lambda10
authentication10
amplify10
api10
aws10
aws-amplify10
graphql10
serverless10
web-development10
cli10
random-forest-classifier9
Programming languages (8)
Github contributions (5)
tejas2008/FakeAccountDetection
May 2020 - Sep 2020
The extensive use of fake information has negative impact on online matrimony. This project helps in finding the fake profiles depending on the information provided by the user
Contributions:9 commits, 8 pushes, 1 branch in 3 months
matrimonypythonmachine-learning-algorithmsfindingrandom-forest-classifier
tejas2008/NoQ
May 2020 - Jul 2021
NoQ helps in reducing the risk of infection both in stores and in queues outside by using virtual queuing system
Contributions:1 PR, 23 pushes, 1 branch in 1 year 2 months
queuesqueuing-systemriskinfectionqueuing
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