I'm a Software Development Engineer II at Amazon, where I lead impactful innovations in AI/ML service integration. I have also contributed to global microservices scalability for the Amazon Luna team. With over four years of experience in high-growth, mission-critical environments, I bring together technical excellence with a strong focus on customer experience and product quality.
I earned my Master’s degree in Computer Science from Texas A&M University with a GPA of 3.9, after completing my Bachelor's in Computer Science from N.M.A.M. Institute of Technology, where I graduated with distinction. I am currently pursing Master in Engineering Management at Trine University.
At Amazon, I've designed and implemented scalable solutions like an automated accounting system for third-party revenue sharing and a feature that prevented nearly 800 duplicate game purchases within two weeks of launch. My work has directly supported Luna’s international expansion and GDPR compliance, processing over 21,000 data requests daily.
Outside of my core role, I’m a published researcher in machine learning for healthcare, with work presented at IEEE conferences. I also serve as a judge for global awards such as the Globee Awards and the Artificial Intelligence Excellence Awards, which has been a great way to stay connected to the broader tech community.
I'm passionate about mentorship and love supporting the next generation of engineers at Amazon. I love to contribute at women in tech community. I thrive on building innovative, high-quality software that scales globally and makes a meaningful impact.
Software Developement Engineer (2021 - present), Seattle
I have nearly four years of experience at Amazon, with over three of those on the Amazon Luna team.
I've had the opportunity to be part of Luna's journey from its early stages through its launch in
the US, Canada, and multiple EU countries. My work focused on enhancing the payment experience,
managing customer entitlements, and ensuring seamless access to third-party content—all key to
creating a smooth and trusted gaming experience for our customers.
Currently, I'm part of the Customer Experience organization, where I work on benchmarking AI/ML
services to help improve service performance, reliability, and intelligence across the board.
My role allows me to drive innovation at the intersection of user experience and cutting-edge
technology, helping shape scalable, global systems that prioritize quality and customer satisfaction.
Interned at Marcus Cloud Infrastructure team at Goldman Sachs, Richardson, TX.
During this internship I built the entire back-end of ECS Dashboard application. This application helped the cloud engineers at Goldman Sachs to quickly view the anomalies in ECS services. I created 3 APIs in Python using Boto3 and Flask. I also learnt about CI/CD pipeline and Containerizing the application using Docker. The entire application was completed within 4 weeks.
Analyzed COVID-19 related tweets and displayed the analysis on a webpage. Used Flume and Kafka to retrieve and store the tweets related to keywords COVID-19 and Coronavirus. Used Spark Streaming to find tweets from most trusted users, find most common hashtags and to find the geographical information of the tweets. Used Flask to get the results from spark and display it on webpage. Used JavaScript to automatically refresh the contents of webpage.
Cleaned the data, built multiple visualization plots to analyse the given flight dataset. Built models for Airline arrival delay prediction using Linear Regression, Lasso, Ridge and Bagged Linear Algorithms.
Developed Deep Neural U-Net Network to transform the gray scale images into colored images. Created classification and regression colorization models and tuned the models based on various hyper-parameters. Created multiple models by changing the loss function, the number of hidden layers and hidden units, and the category of dataset.
Created interactive animations using JavaScript to improve the biology learning experience of middle school students. Integrated these animations into the Stepstone learning environment.
Developed Machine Learning model to predict the Airway difficulties from the simulated patient data. Lead the Mask Ventilation subsystem and developed a Logistic Regression classifier model with an accuracy of 97%. Developed an graphical user interface using PHP, HTML, CSS, JavaScript and Python while allows doctors to use the prediction model.
I did it while being an summer research intern at National Institute of Technology, Karnataka. Conducted independent research and developed a Kannada language dialect identification system using Support Vector Machine classifier with an accuracy of 88.5%. Extracted multiple features from the speech samples to create feature vector and classified the features using various Machine Learning Algorithms.