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BackendArchitectX/README.md

Hi, I'm Pranay 👋

📍 India · ⚙️ Backend Software Engineer · 🧠 Distributed Systems & Performance Engineering

Java Spring Boot Python C++ TypeScript Kafka Redis AWS Docker Kubernetes

Building reliable backend systems that stay understandable under load, failure, and change.

Focused on distributed systems, event-driven architecture, concurrency, performance, and production reliability.


Current Projects

Exact GPU vector-search engine built from CUDA kernels through C++20, Java JNI, and a Spring Boot API. Includes persistent GPU indexes, FP16 storage, production hardening, and reproducible benchmarks.

Java workshop for distributed transactions, progressing through hybrid logical clocks, MVCC, routing, intents, clock uncertainty, read restarts, and serializable conflict prevention.

Spring Boot decisioning system for policy servicing, claims, premium calculation, risk evaluation, renewals, and auditable business decisions.


Open Source

I contribute to infrastructure projects where correctness depends on lifecycle, concurrency, RPC behavior, transaction ownership, and failure recovery.

Merged fix preventing the metrics reporter from starting on controller-only nodes.

PR #30973 coordinating query failure with transaction-commit ownership to address an autocommit race.

PR #4263 making cached RPC connection identity endpoint-aware when a server UID moves to a different host or port.

Contribution focus

Distributed systems correctness · Concurrency and deterministic race testing · RPC and connection lifecycle · Failure recovery · Performance and reliability · Invariant-preserving regression tests


GitHub Activity

GitHub contribution chart


What I'm Doing

Building backend-first systems

Java and Spring Boot services with resilient APIs, transaction boundaries, caching, asynchronous workflows, messaging, and production-oriented failure handling.

Studying distributed systems deeply

MVCC, transaction coordination, clock uncertainty, leader/follower behavior, RPC identity, consistency, concurrency, and failure modes.

Contributing upstream

Working on real bugs in distributed infrastructure instead of limiting engineering work to personal repositories.

Measuring before optimizing

Using profiling, benchmarks, SQL analysis, production telemetry, parallel processing, and regression tests to validate performance changes.


Skills

Languages

Java · J2EE · Python 3 · C++ · TypeScript · JavaScript · SQL

Backend & APIs

Spring · Spring Boot · Spring MVC · FastAPI · Flask · Node.js · Express.js · STL · REST APIs · JSP · ReactJS

Architecture

Microservices · Distributed Systems · Event-Driven Architecture · Asynchronous Processing

Performance

Multithreading · Concurrency · Caching · Functional Programming · Parallel Processing · Algorithms

Messaging & Data

MySQL · PostgreSQL · MongoDB · Redis · Apache Kafka · Message Queues · SQL Query Optimization

Testing & Practices

JUnit · TDD · OOP · SOLID Principles · Agile

Cloud & DevOps

AWS · EKS · PCF · CloudWatch · Docker · Kubernetes · Jenkins · CI/CD

Tools & Monitoring

Git · GitHub · Bitbucket · Jira · Splunk · Dynatrace · XML

AI Tools

Claude Sonnet · Claude Opus · GitHub Copilot · AI-Powered Coding Assistants · Agent-Based Tools


Principles

> Build for failure. Measure before optimizing. Preserve the invariant.

I prefer simple designs with explicit ownership, observable behavior, reproducible tests, and clear failure semantics.


Connect

Pinned Loading

  1. Vortex-CUDA Vortex-CUDA Public

    GPU-accelerated exact vector search engine built with CUDA, C++20, Java JNI and Spring Boot, featuring FP16 storage, batched Top-K search, profiling, benchmarking and production-ready REST APIs.

    Cuda

  2. AutoMQ/automq AutoMQ/automq Public

    Diskless Kafka® on S3. 10x Cost-Effective. No Cross-AZ Traffic Cost. Autoscale in seconds. Single-digit ms latency. Multi-AZ Availability.

    Java 10.7k 770