Senior QA AI Engineer

About the role

At Aristocrat, a global leader in gaming entertainment, we are looking for a Senior QA AI Engineer to transform our quality assurance processes with artificial intelligence. You will work with a dynamic team to design, evaluate and integrate AI‑driven solutions that improve issue detection, risk assessment and overall product quality across our gaming platforms.

Key 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, CI pipelines and product change validation.
• Assess new code against existing patterns, known risks and historical defects, and validate AI‑generated findings for usefulness and explainability.
• Define and roll out AI‑assisted quality gates in core repositories and CI workflows.
• Translate QA knowledge into prompts, playbooks, checklists and evaluation criteria that 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 such as regression analysis, impact assessment, exploratory testing support and release‑risk evaluation.
• Run structured experiments to measure efficiency of AI quality checks and iterate based on real outcomes.
• Contribute to QA ownership shift initiatives, including quality playbooks, scorecard baselines and product‑team enablement.

Required profile

• 5+ years of professional software quality assurance experience on sophisticated products, platforms, games or large‑scale applications.
• Senior‑level QA judgment with strong risk analysis, exploratory testing, defect investigation and release quality evaluation.
• Passion for AI and hands‑on experience using AI tools to enhance analysis, investigation, documentation, testing or workflow automation.
• Familiarity with Git workflows, pull requests, code reviews, CI/CD concepts and modern SDLC practices.
• Ability to distinguish meaningful quality risks from AI noise and to challenge AI outputs critically.
• Excellent communication skills for explaining quality risks, AI findings and improvement opportunities to technical and non‑technical stakeholders.

Required skills

• AI tools
• Git
• CI/CD
• Software Development Life Cycle (SDLC) practices