Stack: Python, Django, DRF, PostgreSQL, Redis, Celery, Docker, Cloud, AI/LLM Integrations

About the Role

We are looking for a Senior Backend Engineer with 4–6 years of hands on experience to build and maintain scalable backend systems for SaaS, AI-enabled workflows, integrations, document processing, payments, and automation heavy platforms.

The ideal candidate is strong in backend architecture, API design, database modeling, async processing, and cloud integrations and actively uses AI coding tools to improve engineering speed, code quality, testing, and debugging.

Key Responsibilities

Design, build, and maintain backend APIs using Django, DRF, and Python
Implement authentication, authorization, JWT-based security, multi-tenant flows, and RBAC
Build scalable services using PostgreSQL, Redis, Celery, Django Channels, and background workers
Integrate with third-party systems such as Stripe, Shopify, Azure, AWS S3, and external APIs
Handle file uploads, Excel/PDF parsing, cloud storage, and data processing
Write clean, well-tested, maintainable code with proper logging and documentation
Work with Docker-based local and production environments
Collaborate with Frontend, Product, QA, and DevOps teams
Review code, optimize queries, and maintain production-quality standards
Use AI tools responsibly for development, testing, debugging, and documentation

Required Skills

4–6 years of backend development experience
Strong proficiency in Python, Django, and Django REST Framework
Solid PostgreSQL skills schema design, indexing, migrations, query optimization
Hands-on experience with Redis, Celery, and async/background jobs
Strong API design REST, serializers, pagination, validation, OpenAPI/Swagger
Experience with JWT, OAuth, social login, RBAC, and secure session handling
Hands-on Docker experience
Third-party API integration and webhook handling
Strong debugging, testing, and production troubleshooting skills
Familiarity with Git, code reviews, branching workflows, and CI/CD basics

AI Coding Practices

Comfort using AI-assisted tools such as GitHub Copilot, Cursor, ChatGPT, Claude, or Codex for:

Generating boilerplate, serializers, tests, and documentation
Reviewing code and identifying edge cases
Writing unit and integration tests
Debugging logs, stack traces, and failing tests
Refactoring legacy code with proper test validation

AI-generated code must always be reviewed, tested, and owned by the engineer. No exposure of secrets, credentials, or production data to AI tools.

Preferred Tech Stack

Node.js and Express.js
Azure Functions, Blob Storage, Queue Storage, Azure OpenAI
AWS S3, boto3, and cloud storage patterns
MongoDB and Mongoose
Django Channels, WebSockets, Daphne
Stripe billing and webhook handling
OpenAI, Anthropic, Groq, LangChain, LangGraph, ChromaDB, RAG, embeddings
PDF, Excel, CSV, and document-processing libraries
pandas, numpy, openpyxl
Testing with pytest, Jest, Playwright
drf-spectacular / OpenAPI
Security rate limiting, CORS, CSP, secret management, webhook verification

Good to Have

Multi-tenant SaaS platform experience
AI agents, LLM orchestration, vector search, document ingestion pipelines
Domain experience in financial, audit, ERP/CRM, incident-management, or workflow automation
Background job reliability, retries, idempotency, and queue-based architecture
Ability to mentor junior engineers

thinQit

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