pipeline: portfolio / stage: intro
Amaresh Kumar
Quality Analyst @ Amazon
Aspiring AWS Cloud & DevOps Engineer skilled in containerizing and deploying full-stack apps on AWS EC2 with Docker, GitHub Actions, and Shell scripting — brings troubleshooting and root-cause analysis skills from professional experience at Amazon.
amaresh@devops:~$ whoami
core toolset
featured projects
CI/CD Pipeline & Infrastructure Automation — Pravesh Estates
Designed and implemented a complete CI/CD and infrastructure automation pipeline for Pravesh Estates, a production real estate platform (FastAPI + MongoDB + AWS S3 backend). Migrated deployment from building directly on the production server to a registry-based model (Docker Hub → EC2), then built a GitHub Actions pipeline that automatically builds, tags, and deploys on every push, with one-click rollback to any previous version by commit SHA. Added a systemd-based health-check watchdog that detects and recovers from unresponsive containers independent of Docker's own restart policy, and automated weekly MongoDB backups to S3 with a rolling 4-week retention policy.
Containerized Deployment & Infrastructure Debugging (Antigravity Wholesale)
Containerized a full-stack wholesale application (React, FastAPI, MongoDB) using Docker and Docker Compose for multi-container deployment. Deployed to AWS EC2 with a live production domain using Docker networking, persistent volumes, and Linux administration. Built a GitHub Actions CI/CD pipeline to auto-deploy every push to main with deployment health checks, and automated EC2 provisioning with Bash scripts to install Docker, configure swap memory, and prepare the environment. Resolved production issues involving Docker builds, memory, disk space, and environment configuration.
recent experience
Quality Analyst · Amazon
September 2025 – PresentFull-time
- Investigated delivery and return defects across multiple regions by analyzing operational data and identifying root causes to ensure accurate case resolution.
- Evaluated failure patterns across large datasets using multiple internal tools to support quality objectives and operational decision-making.
- Applied structured problem-solving to ambiguous, complex cases — determining appropriate resolutions based on documented processes and data interpretation.