Manager, AI-Native Quality Engineering

About Us

When life gets hard, we make it easier! Libra Solutions helps overcome the burdens created by slow-moving legal processes. Combining technical innovation and financial strength, we help speed up cumbersome workflows and ease financial barriers for our customers.

Through the Move Docs personal injury solutions platform, Libra integrates and streamlines medical, financial, and professional services for personal injury cases. Our mission is to help personal injury victims receive the medical care and personal funding needs they require, and to help streamline the process for the attorneys and medical providers that serve these victims. Libra operates under the Move Docs, Oasis Financial and Probate Advance brands.

We are proud of our mission and passionate about applying technology to the challenge of making healthcare more accessible.

Together, under the Libra Solutions banner, we have relationships with over 50,000 attorneys and over 12,000 healthcare providers nationwide, which gives us an amazing platform to service our customers.

The Manager, AI-Native Quality Engineering leads a team focused on improving software quality through AI-powered test automation, regression efficiency, and release quality practices. This role is expected to actively drive the use of AI tools to design, build, maintain, and execute automated tests across API, UI, integration, and end-to-end workflows.

The ideal candidate is a player-coach who can lead a team while staying close to the work. They will evaluate and implement AI tools that improve automated test creation, test maintenance, execution efficiency, defect analysis, and regression effectiveness, while also strengthening release readiness and overall quality outcomes. This role partners closely with Engineering, Product, Dev Ops, and Release stakeholders to improve software delivery and build confidence in product quality.

This position reports to the VP/Director, Engineering and can be based in any of our office locations in Denver, CO, Huntersville, NC, Rosemont, IL or Las Vegas, NV. We welcome strong remote candidates, with occasional travel to Las Vegas as needed.

• 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
• 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…