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
