Building robust APIs, scalable backends & intelligent systems with modern Python technologies
Hi, I'm Arjun Bhul, a Python developer focused on backend development. I build APIs and web applications using Django and FastAPI, and I also work on full-stack projects with Next.js.
I enjoy creating simple, reliable, and scalable backend systems. My work includes API design, database management, authentication systems, and building applications that perform efficiently in real-world use.
Right now, I'm learning FastAPI more deeply, building projects with Django and FastAPI, and exploring AI integration in modern applications. I'm always learning new technologies and improving my backend development skills.
Building scalable, maintainable backend systems with Python frameworks, databases, and modern DevOps practices
Showcasing my backend engineering work โ from API design to deployment
A scalable Django REST API for managing SaaS subscriptions with multi-tenant architecture. Supports organization-based billing, JWT authentication, Stripe-powered payments, subscription lifecycle management, invoice generation, and secure data isolation. Designed for real-world SaaS platforms with production-ready deployment and webhook handling.
A Django REST Framework-based API rate limiting system that enforces per-API-key and per-endpoint request limits using Redis. It features distributed rate tracking with Upstash Redis, secure API key validation, and dynamic request limits across different endpoints. The system returns real-time rate limit headers and is designed for scalable, production-ready backend architectures.
A Python backend API implementing Retrieval-Augmented Generation (RAG) for ISMT College. Built with Flask, featuring RESTful endpoints, integrated with Groq Cloud LLM and ChromaDB for vector storage. Handles document chunking, embedding generation, semantic search, and source-verified responses through a clean API interface. Deployed on Railway with containerized architecture.
A Python backend API for resume screening and candidate ranking. Built with Flask, featuring a clean REST API layer, integrated with Google Gemini 2.0 Flash for AI-generated summaries and all-MiniLM-L6-v2 for semantic similarity matching. Handles file parsing, vector embeddings, and ranked candidate output โ all through a containerized, cloud-deployed backend Railway.
A simple Django web application for managing contacts with full CRUD functionality. Features user-friendly pages like Home, About, and Contact, along with form validation, unique email/phone constraints, and a responsive Bootstrap UI. Includes an admin panel for managing records and a clean interface for viewing, editing, and deleting contacts.
A modern Django-based authentication system with a neon-themed UI. Implements secure login and logout using Djangoโs built-in auth system, along with session handling, error messaging, and password visibility toggle. Features a cyberpunk-inspired responsive design with custom CSS animations and Bootstrap integration. Includes automatic auth-based redirection.
A comprehensive toolkit of cutting-edge technologies and frameworks I use to build intelligent systems and scalable applications.
Let's build robust backend systems together. From REST APIs to database design, I'm ready to architect the server-side of your vision.
Love strategic games and RPGs. Currently mastering chess and exploring AI in gaming.
Devour sci-fi novels and AI research papers. Favorite: "Superintelligence" by Nick Bostrom.
Morning runs and meditation keep me balanced. Marathon training in progress!
Electronic and lo-fi beats fuel my coding sessions. EDM concerts are my recharge.
Exploring backend development through practical implementations, API design patterns, and continuous learning with Python frameworks.
Python is often dismissed as "too slow" for high-performance backends โ but that's wrong. With proper optimization, Python backends handle millions of requests daily. This guide covers profiling, database query optimization, async patterns, Gunicorn/Uvicorn tuning, and Redis caching strategies proven in production environments.
Three frameworks dominate Python backend in 2026: Django, FastAPI, and Flask. FastAPI now has 91,900+ GitHub stars โ surpassing both Flask and Django โ with developer adoption jumping from 29% (2024) to 38% (2025) per JetBrains surveys. This guide uses a practical decision tree to help you choose the right tool based on your workload.
Every API you build needs authentication. In 2026, the choices are clearer than ever: use short-lived JWTs + refresh tokens (15-min access / 7-day refresh) for microservices, OAuth2 for third-party access, and API Keys for public rate-limited APIs. Never store JWTs in localStorage โ use httpOnly cookies to prevent XSS theft.
Master Python asyncio for building high-performance backend services. Learn async/await patterns, event loops, concurrent task execution, and how to handle I/O-bound workloads efficiently without blocking. Includes real patterns used by FastAPI's ASGI foundation.
FastAPI has become Python's fastest-growing web framework, surpassing Flask in GitHub stars in late 2025. Version 0.135.x brings streaming JSON Lines and strict content-type checking with Python 3.10+ as the baseline. Build a production-ready REST API with JWT auth, PostgreSQL via SQLAlchemy 2.0, automated tests, and Docker deployment.
Build secure REST and GraphQL APIs with Python. FastAPI handles the REST layer at 10,000+ requests per second while Strawberry powers GraphQL โ with enterprise GraphQL adoption now at 50โ60%. Covers JWT auth with BOLA checks, rate limiting (API abuses surged ~400%), PostgreSQL, CI/CD, containers, and observability.
Celery 5.6 (Recovery) is the 2026 stable release โ the default distributed task queue for Python, supporting Python 3.9โ3.13 with critical memory-leak fixes. Learn to offload email sending, report generation, and scheduled jobs so your Django/FastAPI views stay fast. Redis serves as both message broker and result backend for simple, high-speed setups.
FastAPI + Docker + uv + vLLM is now the gold standard for production LLM services in 2026. FastAPI gives async performance, vLLM delivers PagedAttention with continuous batching, and Docker ensures reproducibility. Deploy your first production LLM endpoint with the complete pipeline from this guide โ scalable to hundreds of GPUs with zero downtime.
Most Python developers don't fail because they lack syntax knowledge โ they fail because they ignore fundamentals that make code production-ready. From one unhandled exception that caused a payment startup 3 hours of debugging, to silent failures in background workers: this post covers 7 practices that separate scripts from systems in 2026.
Companies in 2026 expect Python developers to go beyond writing scripts โ they look for engineers who understand backend architecture, cloud deployment, DevOps, databases, security, and AI integrations. FastAPI is the top Python framework for 2026: fast, async-ready, and ideal for microservices. This roadmap takes you from fundamentals to production-ready engineering.
Ready to bring your ideas to life? Let's create something amazing together!
I'm always excited to work on new projects and collaborate with fellow developers. Whether you have a project in mind, need consultation, or just want to chat about tech, feel free to reach out!
Typically responds within 24 hours