Senior QA AI Engineer

About the role

Aristocrat is looking for a Senior QA AI Engineer to transform its quality assurance processes using artificial intelligence. You will work with a cross‑functional team to design, implement, and evaluate AI‑driven tools that detect issues, assess risk, and improve QA workflows across our gaming platforms.

Key responsibilities

• Explore, configure, and evaluate AI tools for issue detection, code‑change analysis, and product‑risk identification.
• Build AI‑assisted quality checks for merge requests, peer reviews, CI pipelines, and product change validation.
• Validate AI‑generated findings from a QA perspective, ensuring they are explainable, actionable, and low‑noise.
• Define and roll out AI‑assisted quality gates in core repositories and CI workflows.
• Translate QA expertise into prompts, playbooks, checklists, and evaluation criteria that scale across teams.
• Partner with developers, 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 outcomes.

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 code insight.
• Familiarity with Git workflows, pull requests, CI/CD concepts, SDLC practices, and modern engineering collaboration.
• Ability to critically assess AI outputs, distinguishing meaningful risks from low‑value noise.
• Excellent communication skills for explaining quality risks and AI findings to technical and non‑technical audiences.

Required skills

• Git
• CI/CD pipelines
• Pull‑request and code‑review processes
• AI/ML tooling for software quality
• Software testing and regression analysis