Feb 2023 – Mar 2025 · 2 yrs 1 mo · Seattle, Washington, United States · On-site

• MLE at Amazon General Intelligence org

Jun 2022 – Sep 2022 · 3 mos · New York, New York, United States

Apr 2021 – Sep 2021 · 5 mos

• Full stack developer that owned critical service-
• Owned service responsible for app installation, app management, app migration and app recommendation, app request features on atlassian marketplace and embedded marketplace.
• Part of oncall roaster
• Ideated an app recommendation system using association rule mining technique in Atlassian embedded marketplace. Developed the first backend prototype in Kotlin that constitutes 3.5% of app installs
• Enhanced and owned the core installation legacy service to identify and resolve license generation software defects which reduce app installation failure by 35% approximately saving around 36 Million USD in a year 2021.
• Optimized load management and batch emailing pipeline to launch request app feature on the embedded marketplace which led to 1.2M USD revenue in the following quarter
• Updated MongoDB data model and exposed GraphQL API for the Cloud Fortified App program - to provide end customers a set of extensively secured top 10% of cloud apps
• Tech Stack Used: Java, Kotlin, DynamoDB, React, Javascript, Git, CI/CD, Spring, SpringBoot, React, JQuery, RESTful API,Ansible, CI/CD pipeline, Git, Linux, Agile, Scrum, Docker, DynamoDB, MongoDB, Amazon Web Services, MySQL, Node.js, Microservices, Maven, Gradle, JUnit, Enzyme, Test-driven development (TDD), Redux, Selenium, Cypress, Mockito, Tensorflow, PyTorch, OpenCV, Matplotlib Numpy, Pandas

Jul 2019 – Apr 2021 · 1 yr 9 mos

• Redesigned UI filters on the marketplace homepage by developing reusable React components in Javascript which led to 4.3% increased filter usage
• Created a Kotlin microservice and designed DynamoDB schema to build scalable distributed storage, and query systems that are fault-tolerant and easy to manage to facilitate migration of on-prem apps to the cloud with Rest APIs
• Secured marketplace against privilege escalations and XSS attacks. Implemented the SLOs as API latency and availability of EC2 machines on SignalFX(terraform), providing real-time SLO violation alerts
• Tech Stack Used: Java, Kotlin, DynamoDB, React, Javascript, Git, CI/CD, Spring, SpringBoot, React, JQuery, RESTful API,Ansible, CI/CD pipeline, Git, Linux, Agile, Scrum, Docker, DynamoDB, MongoDB, Amazon Web Services, MySQL, Node.js, Microservices, Maven, Gradle, JUnit, Enzyme, Test-driven development (TDD), Redux, Selenium, Cypress, Mockito, Tensorflow, PyTorch, OpenCV, Matplotlib Numpy, Pandas

Aug 2020 – Sep 2021 · 1 yr 1 mo

• Engineered features and build and trained ML models using machine learning algorithms around the novel problem of detecting mental health problem based on user’s mobile phone app usage. I also constructed end to end data pipelines using Amazon Web Services
• Explored the analysis of mobile usage data as both supervised and unsupervised problem with unreliable labels. Engineered features with the domain experts and performed data analysis on the novel dataset
• Compute mental health score based on mobile phone usage and built machine learning models in python that diagnosed depression with the recall rate of 75% and precision of 71%
• Developed a real-time pipeline using Amazon Web Services Sagemaker to calculate mental health score • optimized a pipeline that ran for 24 hrs to 5 seconds
• Built data pipeline using AWS services (Glue, Sagemaker, DynamoDB, API Gateway) fetching data from mobile application to train machine learning model

Jan 2019 – May 2019 · 4 mos · Greater Hyderabad Area

• Teaching Assistant for Data Structure and Algorithms.

Jul 2018 – Dec 2018 · 5 mos · Massachusetts, United States

• We worked on developing innovative diagnostic tools to address unmet clinical challenges through integrating biology/medicine, micro- and nanotechnology, consumer electronics, and artificial intelligence.
• Successfully designed Convolutional Neural Network models that classify virus with the accuracy of 95% and able to distinguish between sample and control with the accuracy of 97%
• Analyzed Hepatitis B Virus genome sequence using LSTM to predict its resistance towards 5 most common drugs
• Enumerate Neutrophil based on their morphology using Computer Vision techniques for cancer and infection detection

Aug 2017 – May 2018 · 9 mos · Hyderābād Area, India

• Setting up the core for the chapter
• 1) Induction
• ACM inducted 13 new members for the upcoming year. The student were selected based on their coding skills and general computer science aptitude.
• 2) Alumni Network
• We connected with the Computer Science Alumni who were willing to connect with ACM and its activities.
• 3) ACM Constitution
• We with the help of Abhishek Thakur sir made a constitution detailing on following aspects-
• PURPOSE, VISION AND MODUS OPERANDI, Core List of ACM Student Chapter Activities
• Job Description of office bearers, Financial Budget and Management of funds Preamble
• Organizing events for publicizing ACM Student Chapter
• 1) Neural Network Workshop Series
• ACM with the help of a few seniors organized Neural Network workshop series. The participation was high throughout the series. Following classes were organized to help students-
• Introduction
• ANN - Theory and Implementation
• CNN - Theory
• CNN Implementation
• RNN / LSTM Theory
• RNN / LSTM Implementation
• 2) Computer Science Department Project
• We connected with the Computer Science Department who were willing to discuss the project and work they are currently engaged in.
• Faculties were given slots where they discussed their projects, current progress and how students can contribute to the project
• They also informed the student about several future possibilities in their respective field

May 2017 – Jul 2017 · 2 mos · Bangalore

• Project: The aim was to come up with a Maintenance Schedule for fleet management companies that improve the overall reliability and availability of the system while simultaneously maintaining the budgetary constraint.
• Genetic Algorithms: Since the problem was comprised of multiple objectives and their nonlinear structures, we decided to solve the model using multiple objective meta-heuristic Genetic Algorithms(3 Fitness Function)
• Results: Successfully completed the model that presented and analyzed a new mathematical function that models age reduction and improvement factor parameter for repairable and maintainable components