Note: The job is a remote job and is open to candidates in USA. Libra Solutions helps overcome burdens created by slow-moving legal processes through technical innovation and financial services that streamline personal injury workflows. The Manager, AI-Native Quality Engineering leads a team improving software quality through AI-powered test automation, regression efficiency, release quality practices, and collaboration with Engineering, Product, DevOps, and Release stakeholders.
Responsibilities
- 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
- 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
- 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
- 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
- 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
Skills
- 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
- Medical insurance plans
- Dental insurance plans
- Vision insurance plans
- Life insurance plans
- 401k match
- Paid time off
- Strong remote candidates are welcome, with occasional travel to Las Vegas as needed
Company Overview