Senior QA AI Engineer

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

• 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