Deepali Kundwani

About Deepali Kundwani

I am a passionate and results-driven Software Engineer with over 5 years of experience in designing, developing, and deploying scalable web and cloud-based applications. Currently, I serve as a Software Engineer II at Amazon (since October 2024), where I contribute to innovative projects such as microservices for fulfillment optimization, real-time inventory tracking systems, and customer-facing recommendation engines using Java, Python, AWS, and machine learning tools. Previously, at Bosch Global Software Technologies, I progressed from a Software Engineer (20••••22) to a Senior Software Engineer (20••••24), where I led the development of a risk assessment web application supporting users across multiple countries, implemented secure REST APIs with Spring Boot, and migrated legacy systems to AWS, reducing downtime by 40%. My technical expertise spans React.js, Spring Boot, AWS (Lambda, ECS, S3), CI/CD pipelines, and Agile methodologies. In my recent personal project, I developed an E-wallet application (March-June 2024) using React.js, Java Spring Boot, and MongoDB, featuring secure authentication with JWT and automated transaction notifications. I hold a B.Tech. in Computer Science from Rajasthan Technical University. I thrive on solving complex problems, collaborating with diverse teams, and staying at the forefront of technology trends. Let’s connect to discuss innovation in software engineering, cloud solutions, or potential opportunities!

Experience

1. Software Development Engineer Ii

   Amazon

   Oct 2024 — Present · Seattle, WA, US

   Designed and implemented a highly scalable microservices-based system for the Amazon Fulfillment Center, optimizing order processing workflows and reducing latency by 25% using Java, Spring Boot, and AWS ECS.· Developed a real-time inventory tracking API integrated with Amazon’s supply chain system, leveraging AWS Lambda and DynamoDB, improving inventory accuracy by 30% across multiple warehouses.· Collaborated on the development of a customer-facing recommendation engine using Python and machine learning models deployed on AWS SageMaker, increasing personalized product suggestions by 15% and boosting customer engagement.· Automated CI/CD pipelines using AWS CodePipeline and Jenkins, enabling rapid deployment of updates to Amazon’s internal tools, reducing deployment time by 40%.· Enhanced the security of Amazon’s internal authentication system by implementing OAuth 2.0 and integrating it with AWS Cognito, reducing unauthorized access attempts by 35%.

Education

• Rajasthan Technical University

  Bachelor of Technology - BTech

  2015 — 2019