ph3Responsibilities /h3 ul liExplore, configure, and evaluate AI tools for issue detection, code change analysis, product risk identification, and QA workflow improvement /li liBuild practical AI-assisted quality checks for merge requests, peer reviews, continuous integration workflows, and product change validation /li liAssess how new code changes align with existing code patterns, product behavior, known risks, and historical defects /li liValidate AI-generated findings from a QA perspective, ensuring they are useful, explainable, actionable, and not excessive noise /li liHelp define AI-assisted quality gates for Core application repositories and support their gradual rollout in CI workflows /li liTranslate QA knowledge into prompts, workflows, playbooks, checklists, and evaluation criteria that can scale across teams /li liPartner with developers, QA engineers, automation engineers, architects, product managers, and QE Champions to embed quality earlier in the lifecycle /li liIdentify high-value AI-for-QA use cases across regression analysis, impact assessment, exploratory testing support,



and release risk evaluation /li liRun structured experiments to measure the efficiency of AI quality checks and improve them based on real outcomes /li liContribute to QA ownership shift initiatives including quality playbooks, scorecard baselines, and product-team quality enablement /li /ul h3Requirements /h3 ul li5+ years of professional experience in software quality assurance /li liStrong hands‑on experience testing sophisticated products, platforms, games, or large-scale applications /li liSenior‑level QA judgment including significant risk analysis, exploratory thinking, defect investigation, regression assessment, and release quality evaluation /li liKeen passion for AI and direct experience employing AI tools to boost analysis, investigation, documentation, testing, code insight, or routine workflows /li liFamiliarity with Git workflows, pull requests, code reviews, CI/CD concepts, SDLC practices, and modern engineering collaboration /li liAbility to distinguish meaningful quality risks from low-value AI noise and challenge AI outputs critically /li liStrong communication skills with the ability to explain quality risks, AI findings, and improvement opportunities clearly to technical and non-technical collaborators /li liComfortable working in ambiguous areas where processes, tooling, and success criteria are still being defined /li /ul /p #J-18808-Ljbffr

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