Manager, AI Quality – Reliability Engineering

Vizient, Inc

Lead AI Quality Engineering initiatives supporting healthcare solutions at Vizient. Ensure quality and reliability of AI-powered business processes and applications.

Posted 6/18/2026full-timeEdina • Illinois, Minnesota, Texas • 🇺🇸 United StatesSeniorLead💰 $88,900 - $155,500 per yearWebsite

About the role

Key responsibilities & impact

Lead day-to-day AI Quality Engineering activities supporting AI-powered applications, LLM-enabled workflows, intelligent automation solutions, agentic systems, and enterprise AI platforms.
Implement and mature AI Quality Engineering practices, including AI-native testing approaches, validation processes, runtime quality controls, reusable testing accelerators, and scalable testing frameworks.
Coordinate AI validation efforts, including functional testing, prompt testing, workflow validation, regression testing, defect management, release readiness, and production quality assurance.
Partner with AI Engineering, AIOps, LLMOps, Security, Governance, Clinical, Data, and Product teams to ensure scalable and effective quality engineering processes across AI initiatives.
Support runtime quality and reliability through observability, monitoring, telemetry analysis, incident support, drift detection, distributed tracing, and continuous improvement initiatives.
Drive adoption of AI-assisted testing, intelligent automation, reusable test assets, and modern quality engineering practices that improve efficiency and quality outcomes.
Lead delivery coordination activities including sprint execution, testing planning, issue tracking, risk identification, dependency management, and release support.
Collaborate with clinical, operational, and engineering stakeholders to validate healthcare workflows, payer operations, and AI-enabled business processes while supporting responsible AI deployment.
Mentor quality engineers, analysts, contractors, and delivery teams while fostering a culture of continuous learning, engineering excellence, and operational accountability.

Requirements

What you’ll need

Bachelor's degree in Computer Science, Information Systems, Engineering, or related field preferred.
7 or more years of experience in Quality Engineering, Software Testing, Quality Assurance, or related technology disciplines required.
Experience supporting AI-powered applications, machine learning solutions, LLM-enabled workflows, intelligent automation, or emerging AI technologies required.
Experience implementing test strategies, validation frameworks, automation solutions, and quality controls across complex enterprise technology environments required.
Knowledge of AI Quality Engineering concepts including AI validation, prompt testing, model evaluation, runtime assurance, observability, monitoring, and production reliability required.
Experience partnering with cross-functional teams including Engineering, Product, Data, Security, Governance, Clinical, and Operations stakeholders required.
Demonstrated ability to lead testing efforts, manage quality risks, coordinate releases, and support delivery across multiple initiatives required.
Strong analytical, problem-solving, and communication skills with the ability to translate technical quality concerns into actionable business recommendations required.
Experience within healthcare, payer, provider, or highly regulated industries preferred.
Experience with AI-assisted testing tools, test automation frameworks, observability platforms, and modern software delivery practices preferred.
Must be authorized to work in the United States without sponsorship.

Benefits

Comp & perks

Comprehensive benefits plan

ATS Keywords

✓ Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AI Quality EngineeringAI-native testingvalidation processesruntime quality controlsfunctional testingprompt testingregression testingtest strategiesautomation solutionsquality controls

Soft Skills

analytical skillsproblem-solvingcommunicationleadershipmentoringcollaborationrisk identificationdependency managementcontinuous learningoperational accountability