Manager, AI-Native Quality Engineering

Job description

Position Responsibilities

• Team Leadership and People Management
• Manage, coach, and develop a team of quality engineers, automation engineers, and testers
• Set team priorities, goals, and expectations aligned to delivery and quality outcomes
• Support career development, performance management, and skill growth across modern quality engineering practices
• Foster a culture of accountability, continuous improvement, collaboration, and practical execution
• AI-Powered Test Automation
• Drive the use of AI tools to design, generate, maintain, and execute automated tests across API, UI, integration, and end-to-end workflows
• Evaluate and implement AI-assisted capabilities that improve automation speed, coverage, stability, and maintainability
• Help establish practical standards and guardrails for the responsible use of AI in automated test development, execution, and defect analysis
• Help the team use AI to reduce repetitive manual work and improve automation effectiveness across the full test lifecycle
• Guide the application of AI to regression optimization, defect triage, failure analysis, and test maintenance
• Automation and Quality Engineering
• Lead improvements to automated test frameworks and coverage across API, UI, integration, and end-to-end testing
• Stay hands-on with automation design, AI-assisted test generation, framework decisions, and workflow improvements
• Improve regression strategy by using AI and automation to better align test execution with product risk, release timelines, and quality goals
• Partner with engineers to embed AI-enabled automation and quality practices earlier in the development lifecycle
• Improve test reliability, maintainability, and signal quality to support faster and more confident delivery
• Release Quality Management
• Support managed release quality processes, including test readiness, defect review, regression status, and go/no-go input
• Improve release confidence through better quality signals, risk assessment, and operational discipline
• Identify quality risks and escalate issues early with clear recommendations and supporting data
• Partner with engineering and release stakeholders to improve predictability and consistency in software delivery
• Metrics and Continuous Improvement
• Define and track team-level quality metrics such as automation reliability, regression effectiveness, escaped defects, release readiness, and defect trends
• Use data to identify gaps, prioritize improvements, and communicate quality health to leadership
• Drive process improvements that increase efficiency, reduce risk, and improve customer-facing quality

Requirements

• Bachelor’s degree in Computer Science, Software Engineering, or related field, or equivalent professional experience
• 7+ years of experience in software QA, quality engineering, or test automation
• 2+ years of people management or team leadership experience
• Strong hands-on experience building, maintaining, and scaling automated tests
• Strong experience with automation across API, UI, integration, and/or end-to-end testing
• Experience evaluating and using AI assistants such as Claude or similar tools to accelerate test automation and improve automation efficiency, quality engineering workflows, regression effectiveness, and release confidence
• Strong understanding of modern software development practices, CI/CD pipelines, and agile delivery environments
• Experience in SaaS or modern product-based software environments
• Experience partnering closely with engineering, product, and business stakeholders in an agile delivery environment
• Demonstrated ability to lead, develop, and retain a high-performing quality engineering team
• Strong problem-solving, communication, organizational, and coaching skills
• Experience coaching teams through process and tooling changes
• Highly proficient with AI coding assistants (GitHub Copilot, Cursor) and general-purpose LLMs (Claude, ChatGPT, Azure OpenAI) with a demonstrated commitment to building AI-native quality engineering practices and tools evaluation; able to coach quality engineers on effective prompt engineering, AI output evaluation, and responsible AI use
• Prompt engineering proficiency: able to design reusable prompt templates and system instructions for common quality engineering workflows such as test case generation, test data scaffolding, defect analysis, and regression planning
• Skilled at critically evaluating AI-generated test output for correctness, coverage quality, and false confidence patterns; able to identify where AI-generated tests assert the right form but wrong behavior, and guide the team on what to watch for
• Experience leading teams within agentic or AI-accelerated delivery models, or a clear understanding of how agentic workflows change team rhythms, role expectations, and quality ownership when AI agents are contributing to both code and test generation at scale.
• Must be authorized to work in the U.S.

Benefits

• Libra Solutions offers competitive compensation and benefits that include medical, dental, vision and life insurance plans, plus a 401k match and paid time off.