Responsibilities

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- Explore, configure, and evaluate AI tools for issue detection, code change analysis, product risk identification, and QA workflow improvement

- Build practical AI-assisted quality checks for merge requests, peer reviews, continuous integration workflows, and product change validation

- Assess how new code changes align with existing code patterns, product behavior, known risks, and historical defects

- Validate AI-generated findings from a QA perspective, ensuring they are useful, explainable, actionable, and not excessive noise

- Help define AI-assisted quality gates for Core application repositories and support their gradual rollout in CI workflows

- Translate QA knowledge into prompts, workflows, playbooks, checklists, and evaluation criteria that can scale across teams

- Partner with developers, QA engineers, automation engineers, architects, product managers, and QE Champions to embed quality earlier in the lifecycle

- Identify high-value AI-for-QA use cases across regression analysis, impact assessment, exploratory testing support, and release risk evaluation





- Run structured experiments to measure the efficiency of AI quality checks and improve them based on real outcomes

- Contribute to QA ownership shift initiatives including quality playbooks, scorecard baselines, and product-team quality enablement

Requirements

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- 5+ years of professional experience in software quality assurance

- Strong hands‑on experience testing sophisticated products, platforms, games, or large-scale applications

- Senior‑level QA judgment including significant risk analysis, exploratory thinking, defect investigation, regression assessment, and release quality evaluation

- Keen passion for AI and direct experience employing AI tools to boost analysis, investigation, xcskxlj documentation, testing, code insight, or routine workflows

- Familiarity with Git workflows, pull requests, code reviews, CI/CD concepts, SDLC practices, and modern engineering collaboration

- Ability to distinguish meaningful quality risks from low-value AI noise and challenge AI outputs critically

- Strong communication skills with the ability to explain quality risks, AI findings, and improvement opportunities clearly to technical and non-technical collaborators

- Comfortable working in ambiguous areas where processes, tooling, and success criteria are still being defined

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📌 Senior QA AI Engineer (Barcelona)
🏢 Jobtailor
📍 Barcelona