Bio
I'm a Software Development Engineer at
Amazon Web Services,
based in Seattle. I work on the AMI Platform Containers team — part of a lean group responsible for the Fargate containers that power AWS CodeBuild, Batch, and CodePipeline. The work spans end-to-end feature releases, 24/7 on-call operations, and customer issue resolution for what's a $150M ARR business.
Recent work I'm proud of: delivering EKS v1.33 for Windows 17 days ahead of the official launch, leading Windows Server 2025 support for Amazon EKS in v1.35, building a Bill of Materials tool that took diagnostic time from a day to under 5 minutes, and implementing agentic release workflows where Claude Code autonomously executes multi-step packaging and release operations.
Before AWS, I was at
Red Hat
on the OpenShift Engineering team in Raleigh — first as a Software Engineering intern in the summer of 2023, then as an Associate Software Engineer. The highlight was "Orion," a change-point detection platform using E-Divisive statistical methods that cut post-release debugging time by 75+ hours per month across departments. I also served a GPT-based RAG chatbot built with Flask, Langchain, and TypeScript ReactJS that won first place out of 16 teams in Red Hat's hackathon.
I completed my M.S. in Computer Science at the
University of Florida
in December 2023 with a 3.9 GPA, and my B.Tech in Computer Science from
Jawaharlal Nehru Technological University, Hyderabad.
Earlier, I interned at
Honeywell
— first on the SRE Division at Honeywell Connected Enterprises automating testing pipelines, then on the Aero Division migrating their Billing and Subscription system from Internet Explorer to Chromium-based browsers.
I'm AWS Certified Solutions Architect — Associate. Outside of day-to-day work, I'm interested in cloud infrastructure, container ecosystems, AI-assisted developer tooling, and building things that quietly remove manual toil.
Blogs
May 25, 2026 · 6 min read
Placeholder description — to be written.
Publications
Comparative Analysis of Different Pre-trained Deep Learning Models for Brain Tumour Detection
A comparative study evaluating pre-trained deep learning architectures for brain tumour detection from medical imaging — examining accuracy, training cost, and generalization tradeoffs across model families.
The growth and impact of social networks resulted in not only huge collections of data but also a reliable source of information to draw value from. Twitter, one of the major social platforms, is used for collection of tweets related to a disaster. The method discussed in this paper uses bidirectional long short-term memory to classify tweets as original or hoax and obtains an accuracy of 88 percent.
An overview of computer vision algorithms used for facial recognition, exploring an algorithm suitable for biometric attendance systems. Uses histogram-oriented gradients for face detection, face-landmark estimation, support vector machines for recognition, and deep convolutional networks for face comparison. A basic application is included that marks attendance in CSV format. Secured 2nd place at a state-level paper presentation conducted by the Computer Society of India.
Education
Master of Science — Computer Science (Aug 2022 — Dec 2023)
University of Florida, Gainesville
GPA: 3.9 / 4.0
Bachelor of Technology — Computer Science Engineering (Aug 2018 — Jun 2022)
Jawaharlal Nehru Technological University, University College of Engineering, Hyderabad
GPA: 3.9 / 4.0
Work Experience
Software Development Engineer (L4)
Amazon Web Services — AMI Platform Containers, Seattle (Dec 2024 — Present)
Associate Software Engineer & Software Engineering Intern
Red Hat Inc. — OpenShift Engineering, Raleigh (Jun 2023 — Dec 2024)
Software Engineering Intern
Honeywell Technological Solutions — Aero Division, Hyderabad (Feb 2022 — Jun 2022)
Software Engineering Intern
Honeywell Connected Enterprises — SRE Division, Bangalore (Apr 2021 — Jun 2021)