Lead Python Test Automation Engineer
We are looking for a Lead QA Automation Engineer with strong Python expertise and hands-on experience with AI-assisted engineering practices to join our engineering team. In this role, you will combine advanced test automation skills with a modern approach to AI-assisted software quality engineering, taking ownership of QA initiatives and driving improvements across the testing lifecycle.
You will work with LLMs and AI-assisted workflows to decompose complex QA tasks, create and manage engineering artifacts, improve test design and automation efficiency, and critically evaluate AI-generated output. At the same time, you will apply strong engineering judgment to build reliable automation solutions and continuously improve QA practices.
Responsibilities
- Design, develop, and maintain automated tests for user-facing and back-office applications using Python and Pytest
- Develop and maintain UI and REST API automation, expanding automated coverage across different layers of the application
- Apply AI-assisted engineering practices to test analysis, scenario design, automation development, documentation, and other QA activities
- Decompose complex QA and engineering tasks into clear, structured steps suitable for AI-assisted workflows
- Create, use, and manage relevant artifacts as part of AI-assisted development and testing processes
- Analyze requirements, identify gaps and ambiguities, and translate them into clear, testable scenarios
- Apply Specification-Driven Development (SDD) principles and recognize common AI-assisted engineering anti-patterns
- Critically evaluate AI-generated output and validate its correctness, quality, and applicability
- Work with databases, perform SQL queries, and validate data programmatically
- Contribute to non-functional testing and broader QA automation initiatives
- Review and improve existing QA frameworks, processes, and automation practices
- Collaborate closely with developers, engineers, and stakeholders to introduce effective and sustainable QA approaches
- Take ownership of QA initiatives, proactively identify improvement opportunities, and drive them through implementation
Requirements
- Strong commercial experience with Python and test automation
- Solid hands-on experience with Pytest, Pydantic, and Requests
- Strong experience with UI automation, including Page Object Model, CSS selectors, and XPath
- Experience with REST API testing and Allure
- Practical understanding of modern LLMs, including their capabilities, limitations, and guardrails
- Experience applying AI tools in software engineering or QA workflows
- Understanding of how to structure and decompose tasks for effective AI-assisted execution
- Ability to critically assess AI-generated code, test scenarios, documentation, and other artifacts
- Understanding of Specification-Driven Development (SDD) and common AI-assisted engineering anti-patterns
- Basic knowledge of SQL and relational databases
- Strong analytical and problem-solving skills, with the ability to work effectively with incomplete or evolving requirements
- Strong communication and stakeholder management skills, with the ability to drive improvements constructively and handle different perspectives
- High level of ownership, initiative, and ability to work independently
Nice to have
- Experience with Faker, Locust, Allure TestOps, MySQL, or PostgreSQL
- Experience introducing or improving QA practices and automation frameworks within an established engineering environment
- Experience with performance or load testing
- Experience applying AI-assisted approaches to software quality engineering
