Hi, I'm Aryan Agrawal. I’m passionate about building reliable, scalable software, and increasingly, intelligent systems that do real work. I’m currently pursuing my Master’s in Computer Science at Indiana University Bloomington (Spring 2026) and I’m an ex SWE Intern at Gametime Hero. I enjoy shipping production-quality features, building clean APIs and workflows, and turning messy real-world problems into systems that are easy to trust and maintain.

I believe every line of code should create value, improving reliability, saving time, or making complex workflows simpler for users.

My first industry internship = tons of learning beyond just syntax. I got comfortable in a big codebase, wrote small, clean modules, and learned how good pull requests + feedback loops keep teams fast. Team ritual: demo-o’clock, show a tiny win, snag quick feedback, and ship it. Also mastered the ancient art of turning “works on my machine” into “works on everyone’s machine.” 😄

My big win: I owned and shipped a reusable translation/i18n component so the app can support multiple languages consistently. One place to manage copy, instant language switching in the UI, and an easy path for new pages to “just work” with translations, less duplicate effort, more consistency, and future languages made simple.

To keep things sturdy, I improved reliability through testing + CI/CD and helped fix production issues through debugging and log-based investigation. I also wired organizer sign-ups into ClickUp (auto-tasks with context) and cleaned up issues that caused stale screens or duplicate actions, fewer hard refreshes, smoother flows, and happier users.

Tech I enjoyed: Angular + TypeScript (Signals/RxJS), Firebase, Git/GitHub, ClickUp.

Alongside coursework, I’ve done research focused on building data pipelines and applying ML/NLP to messy real-world datasets. I’ve worked on extracting and processing on-chain NFT data (queried via GraphQL) and associated media (IPFS), then running structured analysis and modeling on top of curated datasets. I’ve also built ETL/NLP workflows over large text corpora (e.g., Reddit post-comment hierarchies) and used statistical methods to surface patterns and relationships in the data.

I’ve been going deeper into LLM-powered systems and agentic workflows, how to build automations that are accurate, auditable, and safe in production. I’m especially interested in:

• LLM workflows and agents (tool use, decision routing, human-in-the-loop)
• Retrieval and vector search (RAG-style patterns)
• Building reliable automation with validation, deduplication, and logging

I built an LLM + OCR automation agent to streamline a real front-desk workflow. It turns a manual, error-prone process into a consistent, auditable pipeline.

What it does:

• Parses shipping labels using OCR and an LLM to extract recipient info
• Resolves recipients using a Google Sheets directory (faculty + PhD students)
• Sends the email automatically when confidence is high, otherwise drafts for review
• Tracks status and prevents duplicates using idempotent tracking-ID dedup and logging

Impact:

• Reduced per-label manual effort by ~85%
• Saved ~30+ hours/month and improved consistency for notifications

Explore the project here: https://github.com/aryanag7/LLM-Powered-Package-Notification-Agent

I created a serverless AWS chatbot using AWS Lex, Lambda, and SageMaker to provide cost insights and EC2 rightsizing recommendations. This project taught me how to design reliable cloud workflows with async processing and scalable services.

With 100+ queries processed and ~97% Lambda success rate, I learned how serverless systems can deliver real value when built with reliability in mind.

Explore the project here: https://github.com/aryanag7/AI_ChatBot_Resource_Utilization

I built a full-stack homestay platform to simplify the booking experience for hosts and guests. Using Node.js, Express.js, and MongoDB, I implemented secure authentication, image uploads with Cloudinary, and Mapbox for interactive maps.

This project reinforced how good UX + solid backend engineering can make a product feel “effortless” for users.

Explore the project here: https://github.com/aryanag7/HomeStay

I built a language-agnostic data visualization platform that lets users transform data using Python or R and generate compelling visualizations without being tied to one language.

It supports interactive charts (HTML), static exports (PNG), and 3D visualizations, with real-time preview, download options, and a responsive UI built with Angular and Angular Material.

Explore the project here: https://github.com/aryanag7/Language-Agnostic-Visualization

I like work where reliability, scale, and clarity matter. I enjoy:

• Building and improving user-facing features
• Designing APIs and backend services
• Debugging and improving system behavior in production
• Automating messy workflows with careful validation + fallbacks
• Exploring ML/LLM systems in a practical, product-focused way

Currently, I’m focusing on:

• Building more agentic workflows (tool use, routing, evaluations)
• Learning RAG patterns with vector databases for grounded outputs
• Improving my full-stack depth with React/TypeScript and stronger backend systems

I’m always happy to connect with people building in software, cloud, and applied AI.

• 📧 Email: aryanagrawal2310@gmail.com
• 🌐 LinkedIn: https://www.linkedin.com/in/agrawal-aryan23/
• 📄 Resume: https://drive.google.com/file/d/1j5dyoDpsS--tYLRGFhAvWqZJwzpsTVfg/view?usp=sharing

When I'm not coding, you'll find me:

• Watching Test cricket, my favourite sport to follow
• Enjoying movies and TV series, huge fan of binge-watching
• Playing football, cricket, badminton, and pickleball
• Spending time reflecting, learning, and exploring new ideas