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We're looking for a Lead QA Automation Engineer to join our team in Malaga, Spain in a hybrid working mode. This role blends the strengths of a Lead QA Engineer with those of a Software Developer in Test, enhanced for the AI era. You will combine deep testing expertise with strong engineering skills, building automation frameworks, integrating quality into CI/CD pipelines, and designing advanced test strategies for both traditional and AI-powered software systems.
This is your opportunity to lead quality engineering initiatives in a cutting-edge environment where cloud, automation and AI-driven systems intersect, ensuring the reliability and safety of next-generation applications.
- Analyze requirements and architecture to create comprehensive test plans, cases and data sets across functional, integration, regression and exploratory testing
- Design and maintain automation frameworks using tools such as Cypress, Playwright, Selenium, JMeter, Gatling
- Develop and integrate testing tools into CI/CD pipelines to boost engineering productivity
- Build observability and telemetry solutions for real-time quality monitoring
- Identify and automate repetitive tasks, reducing manual testing effort
- Manage defect lifecycle: reproduce, isolate, document and prioritize issues until resolution
- Apply AI-assisted workflows for test generation, code review and defect triage, while validating AI outputs for accuracy
- Design and execute targeted testing for AI/LLM-powered features, including safety and reliability checks
- Collaborate with developers for quality-first practices across the SDLC and provide leadership in quality strategy
- Work within an agile delivery model, contributing to process improvements and mentoring team members
- Strong experience in software quality assurance covering UI/black-box testing and white-box techniques
- Proficiency in at least one programming language (Java, Python, Scala, or TypeScript)
- Hands-on experience with automation tools (e.g., Cypress, Playwright, Selenium)
- Understanding of SDLC, STLC and agile practices
- Proven experience with CI/CD systems (e.g., GitLab, Jenkins, Azure DevOps, Argo CD)
- Knowledge of cloud services and container platforms (AWS, Azure, GCP, Kubernetes, Docker)
- Familiarity with observability tools (Splunk, Grafana, Prometheus, Datadog)
- Practical experience with AI coding assistants (e.g., GitHub Copilot, Claude Code) for test authoring and debugging
- Familiarity with testing AI features, including evaluation harnesses, golden-set tests, prompt injection safeguards, and model regression strategies
- Awareness of responsible AI principles: bias, fairness, privacy and safety considerations
- Experience designing and validating prompts, LLM workflows, and automation environments with AI integration
- Excellent communication abilities for bug reporting, test planning, and explaining quality trade-offs. Strong analytical and problem-solving skills
- Experience with performance testing (e.g., Gatling, JMeter)
- Prior leadership experience in QA teams or test strategy definition
- Experience in highly regulated or data-sensitive environments