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AbhayR1104/README.md

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Introduction

Hey, I’m Abhay Ravi Kumar a backend and platform engineer who enjoys building systems that actually survive production.

I’ve worked as a software engineer at Hewlett Packard Enterprise (HPE) and Tata Elxsi, where I built and optimized large-scale enterprise integrations and OTT backend platforms used in real production environments.

Over time, I gravitated toward distributed systems, cloud infrastructure, and AI-adjacent backend systems, focusing on scalability, performance, and operational reliability.

I’m currently pursuing my MS in Computer Science at Stony Brook University (Dec 2025) and spend most of my time designing scalable APIs, cloud-native platforms, and RAG pipelines with a strong emphasis on correctness, latency, and system design tradeoffs.

If you like well-designed backend systems, boring infrastructure, and thoughtful engineering decisions you’ll probably like my work.


Technologies & Skills

💻 Languages

C C++ Python Java JavaScript TypeScript

⚙️ Backend & APIs

NodeJS ExpressJS FastAPI Spring Boot REST JWT

☁️ Cloud, DevOps & Architecture

AWS Docker Kubernetes Terraform Helm CI/CD Event Driven

🗄️ Databases & ORMs

PostgreSQL MySQL MongoDB SQLite Redis Prisma Sequelize

🧠 AI, ML & Search

LLMs RAG LangChain Pinecone Vector DB Embeddings Hybrid Search Prompt Engineering Model Evaluation MLOps

🖥️ Frontend

HTML5 CSS3 React React Native Vue.js Angular Next.js TailwindCSS Bootstrap jQuery


GitHub Stats

Abhay Ravi Kumar's GitHub Stats Most Used Languages

Featured Projects

Microservice Cloud DevOps Pipeline A complete GitOps workflow and automated CI/CD pipeline for deploying containerized microservices to AWS.

  • Tech Stack: AWS (EKS, VPC, IAM), Kubernetes, Terraform, GitHub Actions, Docker, Helm, Prometheus/Grafana
  • Key Highlight: Architected a production-grade AWS environment via modular Terraform (IaC) and established a zero-downtime deployment pipeline with multi-layered monitoring via CloudWatch and Prometheus.

LLM Pipeline Evaluation A comparative analysis system testing Direct API, RAG, and Agentic AI workflows using the SQUAD 2.0 dataset.

  • Tech Stack: Python, LangChain, Pinecone (Vector DB), OpenAI GPT-4
  • Key Highlight: Evaluated cost vs. reliability trade-offs across AI architectures, proving that while RAG/Agentic pipelines consume ~3x more tokens than Direct APIs, they establish a critical "performance floor" that virtually eliminates hallucinations.

Pinned Loading

  1. uber-lite-backend uber-lite-backend Public

    A real-time ride-sharing backend API built with FastAPI, PostgreSQL, Redis, and Docker, demonstrating key system design concepts.

    Python 1

  2. LLM-Pipeline-Evaluation LLM-Pipeline-Evaluation Public

    Comparative analysis of Direct API, RAG, and Agentic pipelines using SQUAD 2.0.

    Python

  3. microservice-devops-pipeline microservice-devops-pipeline Public

    A full CI/CD pipeline for microservices using Terraform, Kubernetes, and GitHub Actions.

    HCL

  4. tv-devops-submission tv-devops-submission Public

    TypeScript