Available for hire
v2.0.0

BalajiChaughule

> Python Backend Developer_

I architect and build scalable backend systems, robust REST APIs, and reliable cloud infrastructure. Focused on performance, clean code, and database optimization.

3+
Years Experience
15+
Production APIs
5+
Databases Scaled
2.4k+
GitHub Commits

Professional Summary

I am a Python Backend Engineer dedicated to building scalable, high-performance systems that power modern applications. My engineering philosophy centers on writing clean, maintainable code, making pragmatic architectural decisions, and solving complex data problems.

Over my career, I've architected APIs that handle thousands of requests per minute, optimized database queries to reduce latency by over 60%, and established CI/CD pipelines that streamline deployment workflows. I don't just write code—I build robust infrastructure that businesses can rely on.

API Development

Building robust RESTful & GraphQL APIs with Python (FastAPI, Django). Focusing on clean architecture, versioning, and comprehensive documentation.

Database Engineering

Designing normalized schemas, optimizing complex queries, and managing migrations across PostgreSQL, MySQL, and Redis.

Security & Auth

Implementing secure authentication flows (JWT, OAuth2), Role-Based Access Control (RBAC), and securing endpoints against common vulnerabilities.

Performance

Optimizing response times using caching strategies, asynchronous processing (Celery), and efficient database indexing.

Technical Skills Matrix

A comprehensive overview of my technical expertise, categorized by domain. Proficiency is indicated by the proficiency bars based on years of active usage and project complexity.

01Backend

Python95%
FastAPI90%
Django85%
Flask80%
Node.js70%

02Databases

PostgreSQL90%
MongoDB80%
Redis85%
MySQL75%

03DevOps & Cloud

Docker85%
AWS75%
Linux / Nginx80%
GitHub Actions85%

04Tools & Testing

Git90%
PyTest85%
Postman / Swagger95%
Celery75%

Experience Timeline

My professional journey from learning fundamentals to engineering production backend systems.

Backend Engineer Intern

TechFlow Systems
Jan 2025 - Present

Working on the core microservices architecture. Migrated a legacy monolithic authentication service to a decentralized JWT-based auth flow using FastAPI and Redis, improving authentication latency by 40%.

FastAPIRedisMicroservicesDocker

Freelance Backend Developer

Various Clients
Jun 2024 - Dec 2024

Designed and developed RESTful APIs for mobile applications and SaaS dashboards. Handled database design, implemented payment gateways (Stripe), and deployed containerized apps on AWS ECS.

DjangoPostgreSQLAWS ECSStripe API

Open Source Contributor

Python Community
Jan 2024 - May 2024

Contributed to multiple open-source Python packages. Resolved bugs related to async database connection pooling and improved test coverage by writing comprehensive PyTest suites.

PythonPyTestAsyncIOGitHub

Computer Science Student

University of Technology
2021 - 2025

Focused on Data Structures, Algorithms, Database Management Systems, and Computer Networks. Built foundational projects in Python and C++.

AlgorithmsDBMSNetworkingC++

Featured Backend Projects

A selection of production-grade systems I've architected and built. Focus on scalability, security, and clean architecture.

E-Commerce Backend API

A robust, scalable backend for a modern e-commerce platform handling catalog management, cart operations, secure checkout, and order processing.

The Problem

Needed a reliable system to handle concurrent checkout requests and complex product variations without data anomalies.

Architecture

Microservices pattern using FastAPI for core services, PostgreSQL for relational data, and Redis for cart session management and caching.

Key Achievements

  • Implemented idempotency keys for payment processing
  • Reduced catalog query time by 60% using Redis caching
  • Achieved 99.9% uptime across all endpoints
FastAPI
PostgreSQL
Redis
Celery
Stripe API

Centralized Authentication Service

A standalone OAuth2/OIDC compliant authentication service providing Single Sign-On (SSO) capabilities across multiple internal applications.

The Problem

Managing authentication logic redundantly across 5 different services led to security inconsistencies and maintenance overhead.

Architecture

Built with Django and Django REST Framework, utilizing JWT for stateless session management and PostgreSQL for user data storage.

Key Achievements

  • Unified login across all company domains
  • Implemented Role-Based Access Control (RBAC)
  • Added rate limiting to prevent brute-force attacks
Django
DRF
JWT
OAuth2
Docker

Real-time Task Management System

A collaborative project management tool featuring real-time updates, task assignments, and WebSocket-based notifications.

The Problem

REST polling was causing massive server load and noticeable delays in task status updates between users.

Architecture

Event-driven architecture using FastAPI WebSockets, Redis Pub/Sub for message broadcasting, and PostgreSQL for persistent storage.

Key Achievements

  • Supported 5,000+ concurrent WebSocket connections
  • Reduced server load by 80% compared to previous polling method
  • Implemented offline-sync capabilities
FastAPI
WebSockets
Redis Pub/Sub
SQLAlchemy

High-Performance URL Shortener

A URL shortening service capable of handling high-volume traffic redirects and tracking granular click analytics.

The Problem

Generating unique, collision-free short codes at scale while maintaining sub-50ms redirect latency.

Architecture

Base62 encoding combined with a distributed counter. Caching layer handles 95% of redirect reads before hitting the database.

Key Achievements

  • Handled 10,000+ redirects per second during load testing
  • Implemented geo-location tracking for analytics
  • Created background workers for asynchronous analytics processing
Go/Python
Redis
MongoDB
Nginx

Headless Blog CMS API

A comprehensive Content Management System API supporting rich text, media uploads, scheduled publishing, and revision history.

The Problem

Content writers needed a system that safely handled concurrent edits and provided granular revision tracking without data corruption.

Architecture

Django backend utilizing PostgreSQL JSONB fields for flexible metadata and AWS S3 for media storage via presigned URLs.

Key Achievements

  • Implemented a robust revision control system
  • Optimized image processing using background tasks
  • Built comprehensive API documentation using Swagger UI
Django
PostgreSQL
AWS S3
Celery

AI Resume Analyzer Pipeline

An asynchronous processing pipeline that extracts entities, skills, and experience metrics from uploaded PDF resumes using NLP.

The Problem

Processing PDFs and running ML models synchronously caused API timeouts and a poor user experience.

Architecture

API Gateway pattern routing requests to an async worker pool managed by Celery/RabbitMQ, with results pushed back via WebHooks.

Key Achievements

  • Decoupled heavy ML processing from the main API thread
  • Implemented a retry mechanism for failed processing jobs
  • Achieved 95% accuracy in skill extraction
FastAPI
Celery
RabbitMQ
spaCy
MongoDB

Architecture Approach

My standard approach to building scalable, maintainable backend systems. Decoupling concerns allows for independent scaling and easier debugging.

API Gateway Layer

Nginx / Traefik

Handles SSL termination, rate limiting, and reverse proxying to application servers.

Authentication Layer

JWT / OAuth2

Stateless authentication validating tokens before request reaches core services.

Application / Service Layer

FastAPI / Django

Core business logic execution, validation, and asynchronous task delegation.

Caching Layer

Redis

In-memory caching of frequent queries and session storage to reduce DB load.

Data Persistence Layer

PostgreSQL

ACID compliant relational storage with optimized indexing and connection pooling.

Background Workers

Celery / RabbitMQ

Asynchronous processing for emails, reports, and heavy computational tasks.

API Development Standards

My APIs are built with strict typing, robust authentication, pagination, caching, and rate limiting by default. Here's a realistic example of a production-ready endpoint.

Security First

Endpoints are protected by JWT authentication and granular Role-Based Access Control (RBAC) scopes. Users can only access their own data unless they hold superuser privileges.

Data Validation

Using Pydantic/FastAPI for strict request and response validation. Query parameters are sanitized using Regex to prevent injection attacks and ensure data integrity.

Performance & Caching

Implemented Redis caching with TTLs for frequent reads. Added Rate Limiting decorators to prevent API abuse and ensure fair resource allocation.

FastAPI
JWT Auth
Redis Caching
Rate Limiting
Pagination
Pydantic
api/routes/orders.py
@router.get("/api/v1/users/{user_id}/orders", response_model=Page[OrderResponse])
@requires_auth(scopes=["orders:read"])
@rate_limit(calls=100, period=60)
async def get_user_orders(
    user_id: UUID,
    current_user: User = Depends(get_current_active_user),
    db: AsyncSession = Depends(get_db),
    status: Optional[OrderStatus] = Query(None),
    sort_by: str = Query("created_at", regex="^(created_at|amount)$"),
    page_params: PaginationParams = Depends()
):
    """
    Retrieve paginated orders for a specific user.
    Uses Redis cache if no filters applied.
    """
    # 1. Authorization check
    if current_user.id != user_id and not current_user.is_superuser:
        raise HTTPException(status_code=403, detail="Not authorized")

    # 2. Check Cache
    cache_key = f"orders:{user_id}:{page_params.page}:{status}"
    if cached := await redis.get(cache_key):
        return json.loads(cached)

    # 3. Database Query (Optimized)
    query = select(Order).where(Order.user_id == user_id)
    if status:
        query = query.where(Order.status == status)
        
    orders = await paginate(db, query.order_by(desc(sort_by)), page_params)
    
    # 4. Set Cache (TTL: 5 mins)
    await redis.setex(cache_key, 300, orders.json())
    
    return orders

Database Engineering

The database is the heart of any backend application. I specialize in PostgreSQL and Redis, focusing on schema design, query optimization, and data consistency under high concurrency.

Query Optimization

Analyzing EXPLAIN ANALYZE plans to optimize slow queries. Heavy use of proper indexing (B-Tree, GIN, GiST) and avoiding N+1 query problems in ORMs (Django ORM, SQLAlchemy).

Data Integrity & Normalization

Designing databases to 3NF standard to eliminate redundancy, while selectively denormalizing specific tables when read-heavy performance requirements demand it.

Transaction Management

Ensuring ACID compliance across distributed operations. Implementing pessimistic and optimistic locking strategies to handle concurrent database mutations.

JSONB & Semi-structured Data

Leveraging PostgreSQL JSONB capabilities for schema-less attributes within relational models, including specialized JSONB indexing for fast document retrieval.

db_schema.sql
PostgreSQL
CREATE TABLE users (
id UUID PRIMARY KEY,
email VARCHAR(255) UNIQUE NOT NULL,
created_at TIMESTAMPTZ DEFAULT NOW()
);
CREATE TABLE orders (
id UUID PRIMARY KEY,
user_id UUID REFERENCES users(id) ON DELETE CASCADE,
metadata JSONB,
status VARCHAR(50)
);
-- B-Tree index for foreign key lookups
CREATE INDEX idx_orders_user_id ON orders(user_id);
-- GIN index for JSONB queries
CREATE INDEX idx_orders_metadata ON orders USING GIN (metadata);

DevOps & Deployment

Code isn't done until it's running reliably in production. I handle the full lifecycle from Git push to containerized deployment.

Containerization

  • Multi-stage Docker builds for minimal image size
  • Docker Compose for consistent local development
  • Managing volume persistence and network bridging

CI/CD Pipelines

  • GitHub Actions for automated testing and linting
  • Automated Docker image building and pushing to ECR
  • Zero-downtime deployment strategies

Server & Infrastructure

  • Linux server administration (Ubuntu/Debian)
  • Nginx reverse proxy configuration and load balancing
  • AWS EC2, S3, and RDS provisioning and management

Monitoring & Logging

  • Centralized logging strategies
  • Application Performance Monitoring (Sentry/New Relic)
  • Uptime tracking and alerting setups

System Design Principles

Building systems that survive contact with the real world requires planning for failure, latency, and scale from day one.

Horizontal Scaling & Load Balancing

Designing stateless application layers that can be horizontally scaled behind an API Gateway or Load Balancer. Utilizing Round Robin or Least Connections algorithms depending on the traffic pattern.

Caching Strategies

Implementing Write-Through and Cache-Aside patterns using Redis to reduce database read pressure. Handling Cache Invalidation and mitigating Cache Stampedes via probabilistic early expiration.

Asynchronous Processing

Decoupling heavy tasks (like email sending, report generation, and ML inference) from the main request thread using Message Queues (RabbitMQ/Celery) to maintain sub-100ms API response times.

Database Scaling

Applying read-replicas for heavy read workloads, implementing database connection pooling (PgBouncer), and utilizing logical partitioning for massive tables.

GitHub & Open Source

Active contributor to the Python ecosystem. I believe in giving back to the tools I use daily in production.

balaji3245

2,431 contributions in the last year

Top 3%

Recent Contributions

Fixed an edge case in dependency injection resolution for background tasks.

#10234
Bugfix

Added support for strict trailing slashes in routing configuration.

#2341
Feature

Certifications

AWS Certified Developer – Associate

Amazon Web Services

2024

PostgreSQL Advanced Tuning

DataCamp

2023

Competitive Coding

LeetCode

@balaji32

RankTop 5%
Solved450+

HackerRank

@balaji_c

Rank5 Star Python
Solved120+

Recommendations

"Balaji completely overhauled our legacy database schema, reducing our average query time by over 60%. His understanding of Postgres optimization is exceptional."

S

Sarah Jenkins

CTO, DataFlow Inc.

"Consistently delivers clean, well-documented, and thoroughly tested APIs. One of the most reliable backend engineers I've worked with."

M

Michael Chen

Lead Engineer

Let's Build Together

Looking for a backend engineer to architect your next system or scale your existing API? I'm currently open to new opportunities.

Email

chaughulebalaji09@gmail.com

Send a message →

Location

San Francisco, CA

Remote Available