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
