Jun 2023 – Apr 2025 · 1 yr 10 mos · San Diego, California, United States · Hybrid

• ◦ PerfMonitor interface: Lead the design and development of a cli tool and android application to record performance CPU and GPU metrics from XR devices. Reduced RPC calls made to compositor by 70%.
• ◦ Refactoring XR applications : Refactored and Optimized 11 sample applications to make them 300MB lighter and faster to deploy using C++ and Gradle.

Mar 2023 – May 2023 · 2 mos · Phoenix, Arizona, United States

• ◦ Caching scripts: Reduced unnecessary API calls by implementing local ticket hash storage. Resulted in a remarkable 70% decrease in API calls and significantly optimizing resource usage and server load.
• ◦ MiniVault: Created a secure mini password storage service with encrypted keys and user validation, completely eliminating the storage of passwords in cleartext repositories and hashicorp vault.

Sep 2022 – Dec 2022 · 3 mos · Tempe, Arizona, United States

• Tutoring and Grading the students enrolled in CSE 330(Operating Systems).

May 2022 – Aug 2022 · 3 mos · Austin, Texas, United States

• ◦ iCloud passkey hands-on demo : Created a backend service in Flask to implement WebAuthn specification. Created two different clients, Website in JS and iOS app in swift to test the implementation of iCloud passkey feature and its interaction with WebAuthn service.This was demoed as a proof of concept for future integration in Amazon products.
• ◦ Design of iCloud passkey implementation for Amazon authenticators: Created a high level design document to implement and integrate iCloud passkey technology with the current WebAuthn implementation.
• ◦ Integration with MAP swift app and backend services: The Mobile Authentication Platform(MAP) sample app was integrated with iCloud passkey feature. APIs in the current WebAuthn implementation were upgraded to accommodate the new iCloud passkey feature to reduce customer authentication friction by allowing multiple devices to authenticate.

Jan 2022 – Apr 2022 · 3 mos · Tempe, Arizona, United States

• Tutored and Graded for intro to theoretical computer science(CSE 355).

Aug 2020 – Aug 2021 · 1 yr · Hyderabad, Telangana, India

• ◦ Database Design: Analyzed multiple medical datasets to create a metadata structure to accomodate all possible variations of medical datasets for a future portal that will help medical researchers to easily access data.
• ◦ Covid-19 analysis: Using the datasets provided by Rajiv Gandhi Hospital, India analyzed the relationship between various common metrics and recovery time for hundreds of patients. Disproved hypothesis that recovery time is relevant to patient age using statistical analysis.
• ◦ App for data collection: Created an app using React to collect data for Grace Cancer foundation’s mobile health clinics across villages in Andhra Pradesh.

Mar 2020 – Aug 2020 · 5 mos · Hyderabad, Telangana, India

• Working with IBM and clinysnc to create a database for medical datasets.
• Role is to analyze various medical datasets for pinpointing parameters that help predict mortality rate and effective of various drugs on patients.

Aug 2019 – Nov 2019 · 3 mos · Hyderabad, Telangana, India

• Teaching assistant for the course digital systems and microsystems.

Aug 2019 – Jul 2021 · 1 yr 11 mos · Bangalore Urban, Karnataka, India

• ◦ Development and maintenance of REST APIs: Used Java and Springboot to create and manage APIs used to pack and send products from JIO warehouses nationwide.
• ◦ Creation of document services: Created a new service to sign and send shipment invoices periodically for different business types (B2B, B2C, etc) to be stored in the SOM (Store order management) database.
• ◦ Azure pipeline creation and maintenance: Setup azure pipelines for various products for easier build and
• deployment for the rest of the team.

May 2019 – Jul 2019 · 2 mos · Gurgaon, Haryana, India

• ◦ Restaurant dataset creation: Created datasets containing data from restaurants from all major cities in India using MySQL to find effectiveness of advertisements using major attributes like advertisement duration, location and clicks.
• ◦ Creation and analysis of machine learning models: Used Deep Wide network, Random Forest, XGBoost and LSTM with ANNs models to run on the dataset created to find the popularity and overall success of restaurants with and without advertisements.
• ◦ Results of analysis: XGboost model increased accuracy from 0.79 to 0.87. This model was used in the Ads dashboard for future contracts with merchants and restaurants in v14 of the app.

Jan 2019 – Apr 2019 · 3 mos · Hyderābād Area, India

• Teaching assistant for the course engineering systems.

Jun 2018 – Jul 2018 · 1 mo · Gurgaon, India

• Creating a one stop portal for employees to download software.

Sep 2015 – Feb 2016 · 5 mos · Noida Area, India

Jun 2011 – Feb 2014 · 2 yrs 8 mos · Noida Area, India