- End-to-end ML pipeline converting grayscale SAR satellite images to colorized outputs for agricultural and geographic analysis.
- Deployed FastAPI + Docker REST API on AWS with sub-second latency and 99%+ uptime.
- Automated CI/CD via GitHub Actions, cutting deploy time by 80%.
ARNAV BHATT
- [LOCATION]GREATER NOIDA, INDIA
- [STATUS]Open to SDE / Backend Internships
- [MISSION]Build. Deploy. Scale. Repeat.
Hi people!
I'm Arnav — a Backend Developer and Cloud Engineer who also loves building ML pipelines. Currently pursuing B.Tech in CSE with a Cloud Computing specialization at Bennett University.
I ship scalable REST APIs with FastAPI, run containerized services on AWS (ECS, ECR, Lambda), and automate everything with Docker + GitHub Actions. Open to SDE internships and backend/cloud roles.
Side projects on AWS — exploring ECS service mesh patterns and event-driven backends with SQS + Lambda.
System design deep-dives, distributed databases, and sharpening DSA in C++ for interviews.
Land an SDE / Backend internship at a product company shipping at scale.
Late-night coding sessions, lo-fi beats, and breaking things in staging before they break in prod.
BACKEND ENGINEER INTERN
6-month backend internship contributing to APIs, databases, and server-side applications. Working with cloud services and related backend technologies alongside the engineering team.
BACKEND DEVELOPER
Maintained AWS infra (EC2, ECS, CloudWatch). Containerized backend services with Docker for consistent dev/prod deploys. Built & debugged FastAPI REST APIs with zero-disruption deployments.
- Random Forest on UCI Heart Disease dataset achieving 77% accuracy; deployed as live REST API on Railway.
- Responsive Vite frontend consuming the Railway-hosted ML backend.
- Reduced build time by ~40% and delivered end-to-end inference across all device sizes.
- Cloud-native deployment platform on AWS ECS/ECR with Docker containers.
- CloudWatch alarms for proactive anomaly detection.
- Automated CI/CD via GitHub Actions + Vercel — zero-downtime releases, 90% less manual overhead.
Agentic RAG
- Currently building an agentic Retrieval-Augmented Generation system with tool-using LLM agents.
- Multi-step reasoning over a vector store with autonomous retrieval and self-correction loops.
- Work in progress — architecture and benchmarks coming soon.
B.Tech, Computer Science Engineering
- Specialization: Cloud Computing
- Coursework: DSA, Algorithms, Complexity Analysis, Cloud Architecture, DBMS, OS, Networks, ML, OOP
Class XII — Senior Secondary
- Completed 12th in 2023 · PCM stream
AWS Academy & Coursera
- AWS Academy: Cloud Foundations, Architecting, Developing, Data Engineering, GenAI, Security
- Databases and SQL for Data Science — Coursera / IBM
Use arrow keys or WASD · don't bite yourself 🐍
love the doodle board!
ship it 🚀
AWS gang 💪
* notes live in your browser only — be nice 💛
- AWS Academy Cloud Architecting (Mar 2025)
- AWS Academy Cloud Developing (Mar 2025)
- AWS Academy Data Engineering (Mar 2025)
- AWS Academy Generative AI Foundations (Oct 2025)
- AWS Academy Cloud Foundations (Nov 2024)
- AWS Academy Cloud Security Foundations (Oct 2024)
- Databases & SQL for Data Science — Coursera / IBM (Nov 2024)
- Strong DSA & OOP fundamentals in Python
- Active on GitHub — github.com/ArnavBhatt30
- Built & deployed 4+ production ML/backend systems
- Exploring cloud-native architecture & distributed systems
- Tinkering with new AWS services as they launch
- Learning by shipping — every project goes live