DC
Software Engineering • AI Systems

Hi, I'm Divyansh Chandarana

Computer Science @ Arizona State University

Building production software at the intersection of backend systems and applied AI. Shipping high-volume services and reliability-first tooling—from Payments APIs at Amazon to accessibility-first transcription platforms at ASU—turning messy operational workflows into clean, scalable systems.

Backend & Distributed Systems

AWS, Java/Python APIs, reliability, observability, rollout safety

AI Systems in Production

ASR pipelines, task queues, evaluation, accessibility-first outputs

Full-Stack + iOS Delivery

Next.js + FastAPI apps, SwiftUI products, real UX and edge cases

4

Shipped Projects

2

Roles

Live

Production Systems

About

Impact-driven engineering, shipped in production

What I Work On

Focused on backend/cloud engineering and applied AI systems where reliability matters. Building APIs, pipelines, and automation that reduce manual load, improve observability, and ship safely under real traffic. Recent work includes a production transcription platform used across live university programs and a distributed Payments API at Amazon operating at high daily volume.

How Impact Is Measured

Optimizing for outcomes that teams feel: faster debugging, fewer manual steps, clearer metrics, and systems that stay stable as usage scales.

Key Highlights

  • Shipped a distributed Payments compliance API at Amazon handling 250K+ requests/day, deployed with staged rollouts and zero-downtime releases
  • Improved performance and reliability with observability-first instrumentation—cutting p99 latency by 17.8% and speeding detection/recovery by 22%
  • Built an accessibility-first transcription platform at ASU serving 12,760+ learners, standardizing output quality and reducing manual operations
  • Led Claude Code enablement across cross-functional course teams—Canvas-connected workflows, troubleshooting, and guided adoption sessions
  • Delivered a SwiftUI study spot finder with MapKit + filtering + offline favorites + Apple Maps deep linking

Featured Projects

Production systems and products—backend, AI, and mobile

Featured

AI Transcript Platform

Accessibility-first transcription → clean, WCAG-friendly HTML at scale

Production transcription pipeline for university programs: file ingestion, background processing, and structured HTML generation for screen readers and web delivery.

Next.js 15FastAPIAWS ECSAWS S3+6
Featured

Claude Code Enablement

Agentic AI adoption → Canvas-connected workflows for course teams

Department-wide rollout enabling non-technical course teams to use agentic AI workflows through Claude Code with Canvas LMS integration.

Claude CodeCanvas APIMCPWorkflow Automation+1
Featured

SignalQ

QR-based feedback + rewards → multi-tenant SaaS for local businesses

Dynamic QR flows that route customers to location-specific feedback and incentives, backed by a business dashboard for analytics and follow-up.

ReactTypeScriptFlaskFirestore+3
Featured

StudySpace AZ

Native iOS study spot finder → map + smart filtering + offline favorites

SwiftUI app using CoreLocation + MapKit + SwiftData to surface study-friendly venues with search, scoring, persistence, and deep links to Apple Maps.

SwiftUICoreLocationMapKitSwiftData+2

Professional Experience

Shipping impact across Payments, education, and internal tooling

Amazon

Software Development Engineer Intern

May 2025 - August 2025
Tempe, AZ

Delivered a distributed Compliance API on AWS with production observability, rollout plans, and performance tuning under real traffic.

  • Owned an end-to-end API service deployed to production, processing 250K+ daily requests across global marketplaces
  • Instrumented CloudWatch metrics, structured logs, and alerts—improving failure detection and recovery speed by 22%
  • Reduced p99 latency by 17.8% via async handling, targeted refactors, and caching strategies
  • Worked through design reviews, code reviews, and staged rollouts to meet availability, security, and performance requirements
AWSJavaDistributed SystemsCloudWatchREST APIsReliabilityObservability

Arizona State University (Learning Enterprise)

Research Analyst / Software Developer

January 2024 - Present
Tempe, AZ

Building production AI + automation workflows that reduce operational overhead and improve accessibility across live university programs. Also leading Claude Code enablement for cross-functional course teams.

  • Built and deployed an AI transcription platform supporting live program delivery, improving accessibility for 12,760+ learners
  • Implemented pipeline and ETL workflows across Python/SQL/AWS/Docker for ingestion, preprocessing, and analytics delivery
  • Drove reliability improvements through metrics, monitoring views, and stakeholder-ready reporting for accuracy/latency/usage
  • Led migration work toward lower-latency, lower-cost inference infrastructure (on-prem / Apple silicon)
  • Led Claude Code + Canvas MCP enablement across course design teams—setup, troubleshooting, and guided adoption sessions
PythonAWSDockerETLData PipelinesAI SystemsAccessibilityClaude CodeCanvas API

Skills & Expertise

A practical toolkit across backend, cloud, and applied AI—built for shipping

Skill Proficiency

Tech Stack Distribution

Technical Skills

Languages & Frameworks

PythonGoJavaC++SwiftC#JavaScript (ES6+)TypeScriptSQLReactNode.jsFlaskFastAPISpring BootSwiftUI

Cloud & Infrastructure

AWS (EC2, Lambda, S3, CloudWatch)DockerGitHub ActionsLinux/Unix on x86GitCI/CD Pipelines

Engineering Fundamentals

Backend DevelopmentDistributed SystemsREST API DesignData PipelinesObservability & MonitoringScalabilityReliabilityTestingSDLC

Applied AI Systems

ASR PipelinesTranscription Evaluation (WER/CER)Task QueuesPrompt-Driven AutomationAccessibility-First Formatting

Let's Connect

Interested in collaboration or just want to chat about tech?

Email

divyanshc097@gmail.com

LinkedIn

Divyansh Chandarana

GitHub

@dchandarana07

Location

Tempe, Arizona

Download Resume
Open to Opportunities

Open to full-time roles starting Summer 2026.
Interested in Software Engineering, Backend/Cloud, and Applied AI Systems.