Abhi Sridhar.

Software Development Engineer

Amazon Web Services • Full-Time & Intern • May 2023 – Present

Arlington, VA

I design and deliver backend APIs and orchestration workflows for hybrid-cloud connectivity, build large-scale deployment automation for Outposts services, and develop containerized integration tests and provisioning logic for Gen2 Outposts components. I also own on-call, deep-dives, and incident response for these services.

As an intern (May – August 2023): deployed APIs with serverless-orchestration and created IAM roles to invoke Lambda in any Availability Zone; designed DynamoDB schema and implemented Lambda for the service team account registry with read/write paths and improved security; created CI/CD pipelines with CloudFormation stacks to automate AWS account creation and IAM role bootstrapping for Outposts service teams.

Software Engineer Intern

Qualcomm • Intern • May – August 2021 & May – August 2022

Austin, TX & San Diego, CA

In San Diego in summer 2022, I developed an internal tool in React to automate modem power subsystems’ KPI extraction, analysis, and visualization of changes across use cases. I implemented a Kubernetes solution to deploy APIs and UI as microservices in Docker containers on Linux VMs for increased availability and security, and designed, implemented, and deployed Flask APIs in Python for CRUD on power data and crash scope report data in MongoDB.

In Austin in summer 2021, I developed an internal tool in Angular to automate the firmware code gates request and approval process when reviewing and integrating changes. I designed, implemented, and deployed Flask APIs in Python for CRUD on SQL databases and designed a history-tracking table schema.

Graduate Teaching Assistant

The University of Texas at Austin • Part-Time • Aug 2022 – May 2024

Austin, TX

I was a graduate teaching assistant for the Software Engineering & Design Lab, where I helped teach API design, software architecture, and full-stack development to upper-division undergrads. I ran discussion sections, held office hours, and graded projects and assignments.

This project presents a novel application of diffusion models to adapt neural network parameters for handling data distribution shifts. Using a hypernetwork-based approach, diffusion models generate parameters tailored to target domains, demonstrated on image classifiers facing various corruptions. The method adapts parameters for effective performance on both known and unseen corruptions, offering a flexible alternative to traditional fine-tuning when target domain data is scarce.

This project uses Q-learning and Deep Q-Networks (DQNs) to train a reinforcement learning agent for the first ten levels of Bloxorz. It compares memory use, computational complexity, and convergence time between tabular Q-learning and DQN models. The study finds that tabular Q-learning, especially with a by-level approach, is more efficient than DQNs for this task, enhancing the application of RL techniques to discrete, state-space-constrained environments.

ChainLife is a blockchain application for healthcare insurance management. It provides a decentralized and transparent network to securely share patient data and automate claims. Using smart contracts, ChainLife ensures efficient data handling and rewards users with CLIFE tokens for maintaining healthy lifestyles. The user-friendly interface enhances healthcare quality and accessibility while protecting patient privacy.

HitStick Technologies creates a durable, reliable collision-sensing system for football helmets designed to enhance player safety. The system features unique capabilities, including real-time impact detection and analysis, easy integration into existing helmets, and competitive pricing. By improving safety measures, HitStick aims to support youth participation in contact sports and contribute to overall player safety.