Responsibilities

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

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, 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

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