Manager, AI-Native Quality Engineering

Libra Solutions

1d•Hybrid

About The Position

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, DevOps, and Release stakeholders to improve software delivery and build confidence in product 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.

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