QA Automation Manager

About the role

This role will report onsite to our HQ in San Diego, CA. Axos Bank is seeking a QA Automation Manager who serves as a strategic leader driving transformative change within the Centers of Excellence organization.

Responsibilities

• Define and execute the QA strategy to expand the use of AI, automation and manual testing frameworks, including security/vulnerability testing and code coverage measures and improvements.
• Develop an executable QA roadmap with milestone deliverables, success metrics, and stakeholder alignment.
• Drive AI-assisted test generation, automation conversion, and resilient/self-healing automation to reduce maintenance and flakiness.
• Establish continuous quality evaluation, monitoring, and feedback loops, including quality gates and regression controls.
• Partner with Engineering and DevOps to embed quality into CI/CD pipelines and enforce release standards.
• Define and enforce quality standards, acceptance criteria, and release gating rules across teams.
• Lead end-to-end testing strategies across functional, performance, regression, web, mobile, and API layers.
• Oversee defect management, root-cause analysis, and preventative quality improvements.
• Analyze quality metrics, build actionable dashboards, and proactively address quality and delivery risks.
• Ensure compliance through regular quality audits and alignment with regulatory and risk management practices.
• Present quality insights, risks, and recommendations to senior leadership.
• Optimize testing processes and promote continuous improvement.
• Collaborate with product and business teams to incorporate customer feedback into quality outcomes.
• Lead, develop, and scale QA leadership and teams, building organizational capability in automation and AI-assisted Quality Engineering.

Requirements

• Bachelor’s degree required
• 7+ years' of experience in Quality Assurance, including experience managing teams.
• 1+ years' of experience in financial services or related regulated industry.
• Strong understanding of SDLC and testing methodologies, including functional, performance, regression, automation tools, and CI/CD pipeline integration.
• Hands-on expertise building and maintaining test automation for web and mobile applications, APIs, and test data.
• Proven use of code coverage and quality analysis platforms such as SeaLights, SonarQube, or similar tools.
• Demonstrated success applying AI and GenAI to accelerate Quality Engineering, including AI-assisted test generation, AI-driven test data creation, and conversion of manual tests into automation.
• Track record of implementing resilient and self-healing automation patterns to reduce flaky tests and improve reliability.
• Working knowledge of LLMOps-style lifecycle practices for AI-enabled systems, including evaluation, validation, deployment gates, monitoring, and feedback loops.
• Experience embedding security and vulnerability testing into automated pipelines.
• Awareness of AI application security risks and testing approaches, including guardrails for prompt injection and sensitive data leakage.
• Understanding of AI risk management frameworks and governance concepts, such as NIST AI RMF, to support compliant adoption in regulated environments.

Qualifications

• Define and execute the QA strategy to expand the use of AI, automation and manual testing frameworks, including security/vulnerability testing and code coverage measures and improvements.
• Develop an executable QA roadmap with milestone deliverables, success metrics, and stakeholder alignment.
• Drive AI-assisted test generation, automation conversion, and resilient/self-healing automation to reduce maintenance and flakiness.
• Establish continuous quality evaluation, monitoring, and feedback loops, including quality gates and regression controls.
• Partner with Engineering and DevOps to embed quality into CI/CD pipelines and enforce release standards.
• Define and enforce quality standards, acceptance criteria, and release gating rules across teams.
• Lead end-to-end testing strategies across functional, performance, regression, web, mobile, and API layers.
• Oversee defect management, root-cause analysis, and preventative quality improvements.
• Analyze quality metrics, build actionable dashboards, and proactively address quality and delivery risks.
• Ensure compliance through regular quality audits and alignment with regulatory and risk management practices.
• Present quality insights, risks, and recommendations to senior leadership.
• Optimize testing processes and promote continuous improvement.
• Collaborate with product and business teams to incorporate customer feedback into quality outcomes.
• Lead, develop, and scale QA leadership and teams, building organizational capability in automation and AI-assisted Quality Engineering.