We're looking for an experienced Senior Django Developer with strong QA automation experience to help us build, maintain, and scale Python/Django applications while strengthening integration testing across our product portfolio.
This role is adecuado for a backend engineer who is comfortable working deep in a Django/Django REST Framework codebase, but who also understands how to design reliable automated tests around UI, API, and integration flows. You'll be embedded in a Python/Django engineering environment, contributing to application development while helping expand our black-box integration testing coverage and supporting a future migration from Selenium to Playwright.
What you'll work on:
- Implementing our established integration testing framework into new products
- Supporting the integration testing backlog across existing products (Selenium-based)
- Writing and maintaining Gherkin feature files and Python Behave test specs
- Running and maintaining CI pipelines via TeamCity and GitLab MRs
- Accurately mocking AWS services (localstack, moto) to isolate test environments
- Contributing to a future Playwright migration roadmap
- Have strong hands-on experience building backend applications with Python and Django
- Are comfortable working with Django REST Framework and API-driven architectures
- Have hands-on experience with black-box integration testing using mocks to isolate the system under test
- Have written or maintained Gherkin feature files and Behave test suites
- Are comfortable configuring GitLab pipelines for continuous testing as part of MR workflows
- Understand AWS service behavior well enough to mock it accurately in test flows
- Have worked with Docker in a testing or CI context
- Playwright experience is a plus, we're planning a migration and want someone who can help lead it
AI-powered QA:
We're building toward an AI-augmented testing practice, and we want engineers who are already thinking that way. The ideal candidate is familiar with how AI can be applied across the QA lifecycle; from intelligent test generation and self-healing selectors, to using LLMs to analyze test failures, expand coverage from specs, and reduce maintenance overhead on flaky tests. Experience with AI-assisted tooling (Copilot, Cursor, or QA-specific tools) is a plus; what matters most is that you understand where AI adds real value in an integration testing context versus where it doesn't.