AVP, AI Quality & Reliability Engineering

Vizient•Edina, MN

1d•$156,500 - $290,100•Onsite

About The Position

In this role you will lead the strategy, operationalization, governance alignment, and continuous evolution of enterprise AI Quality Engineering capabilities across Vizient. You will establish scalable AI Quality Engineering operating models, validation frameworks, runtime quality practices, synthetic data ecosystems, simulation-driven testing capabilities, and enterprise quality standards supporting responsible industrialization of AI-powered business solutions at enterprise scale and enterprise AI test data modernization, synthetic data platforms, healthcare digital simulation ecosystems, and hospital twin capabilities supporting secure, realistic, and scalable AI validation. You will Partner closely with AI Engineering & Delivery, AI Operations, Governance, Security, Clinical, Data, and Business teams to ensure AI solutions are secure, reliable, observable, compliant, scalable, and aligned with enterprise quality and healthcare operational expectations. In this role you will combine enterprise quality engineering leadership, AI-enabled testing modernization, healthcare workflow validation, governance alignment, and cross-functional organizational leadership.

Requirements

Combine enterprise quality engineering leadership, AI-enabled testing modernization, healthcare workflow validation, governance alignment, and cross-functional organizational leadership.
Experience with AI-powered applications, LLM-enabled workflows, intelligent automation solutions, agentic systems, and enterprise AI platforms.
Experience establishing and maturing AI Quality Engineering capabilities, including AI-native testing strategies, validation frameworks, runtime assurance practices, scalable operating models, and reusable quality accelerators.
Experience modernizing traditional Quality Engineering practices to support AI-enabled workflows, probabilistic systems, intelligent orchestration, and evolving healthcare operational workflows.
Experience defining enterprise AI quality standards, testing methodologies, validation approaches, release readiness criteria, and governance-aligned quality practices supporting scalable AI adoption across the enterprise.
Experience providing executive oversight across AI validation, quality engineering, test automation, runtime quality monitoring, release readiness, and quality improvement initiatives.
Experience leading organizational transformation efforts supporting the evolution of traditional QA capabilities toward AI-native quality engineering and simulation-driven validation practices.
Experience leading enterprise AI validation strategies, including functional validation, prompt testing, workflow testing, regression testing, runtime quality assurance, and production reliability practices.
Experience partnering with AI Engineering, AIOps, LLMOps, Security, Governance, Clinical, and Data teams to establish scalable quality engineering processes supporting enterprise AI development lifecycle management and production operationalization.
Experience supporting enterprise AI runtime quality practices, including telemetry integration, monitoring alignment, incident coordination, release validation, deployment readiness assessments, and runtime reliability improvement initiatives.
Experience driving modernization of enterprise test automation capabilities leveraging AI-assisted testing, intelligent automation, reusable testing accelerators, scalable quality engineering frameworks, and measurable quality metrics.
Experience supporting enterprise AI observability and evaluation initiatives to improve reliability, traceability, runtime visibility, and operational confidence across AI-enabled systems.
Experience collaborating with Clinical, Operational, and Engineering stakeholders to support validation approaches for healthcare workflows, payer operations, and AI-enabled business processes.
Experience leading enterprise strategies supporting AI test data modernization, synthetic data capabilities, simulation environments, and scalable testing ecosystems enabling secure, realistic, and enterprise-grade AI validation.
Experience overseeing development of enterprise synthetic test data platforms, healthcare simulation ecosystems, and hospital twin environments supporting AI engineering, testing, validation, and deployment readiness.
Experience partnering with Data, Clinical, Engineering, Security, Governance, and AI teams to support scalable simulation frameworks, realistic operational testing scenarios, PHI-safe validation environments, and AI-enabled testing acceleration initiatives.
Experience supporting development of simulated healthcare operational environments and hospital twin capabilities enabling workflow validation, operational stress testing, scenario simulation, and safe evaluation of AI-enabled healthcare processes.
Experience driving scalable approaches for synthetic data governance, simulation fidelity, test environment automation, and secure testing ecosystem management within regulated healthcare environments.
Experience partnering with AI Governance, Risk, Compliance, Security, Clinical, and Operational stakeholders to support responsible AI practices, operational safeguards, human oversight controls, validation traceability, auditability, and secure deployment of AI solutions.
Experience supporting development and implementation of enterprise AI quality controls, validation evidence processes, testing governance frameworks, risk mitigation practices, and quality review standards aligned with enterprise and regulatory expectations.
Experience establishing scalable processes supporting secure, compliant, reliable, and measurable AI solution deployment within regulated healthcare environments.
Experience leading AI Quality Engineering portfolio activities, including organizational planning, vendor coordination, contractor oversight, delivery prioritization, execution governance, and scalable capability development.
Experience partnering with enterprise stakeholders to evaluate implementation readiness, quality risks, scalability considerations, testing feasibility, and delivery dependencies for prioritized AI initiatives.
Experience supporting tooling evaluations, platform assessments, automation strategies, and modernization initiatives supporting enterprise AI Quality Engineering maturity.
Experience defining scalable squad structures, delivery engagement models, support processes, and governance approaches supporting enterprise AI transformation initiatives.
Experience helping drive organizational capability development, workforce maturation, operating model evolution, and enterprise adoption of modern AI Quality Engineering practices.
Experience leading, mentoring, and developing quality engineering leaders, managers, engineers, analysts, and contractor teams while fostering a collaborative, continuously learning, and engineering-driven culture.
Experience communicating quality risks, testing strategies, governance implications, implementation tradeoffs, and strategic recommendations effectively to both technical and executive stakeholders.
Experience promoting a culture of engineering excellence, continuous improvement, enterprise accountability, responsible AI adoption, and operational discipline.
Experience researching and evaluating emerging AI quality engineering, testing, observability, validation, simulation, automation, and runtime assurance technologies supporting innovation and continuous improvement initiatives.

Responsibilities

Lead enterprise AI Quality Engineering initiatives across Vizient, including AI-powered applications, LLM-enabled workflows, intelligent automation solutions, agentic systems, and enterprise AI platforms.
Establish and mature AI Quality Engineering capabilities, including AI-native testing strategies, validation frameworks, runtime assurance practices, scalable operating models, and reusable quality accelerators.
Modernize traditional Quality Engineering practices to support AI-enabled workflows, probabilistic systems, intelligent orchestration, and evolving healthcare operational workflows.
Define enterprise AI quality standards, testing methodologies, validation approaches, release readiness criteria, and governance-aligned quality practices supporting scalable AI adoption across the enterprise.
Provide executive oversight across AI validation, quality engineering, test automation, runtime quality monitoring, release readiness, and quality improvement initiatives.
Lead organizational transformation efforts supporting the evolution of traditional QA capabilities toward AI-native quality engineering and simulation-driven validation practices.
Lead enterprise AI validation strategies, including functional validation, prompt testing, workflow testing, regression testing, runtime quality assurance, and production reliability practices.
Partner with AI Engineering, AIOps, LLMOps, Security, Governance, Clinical, and Data teams to establish scalable quality engineering processes supporting enterprise AI development lifecycle management and production operationalization.
Support enterprise AI runtime quality practices, including telemetry integration, monitoring alignment, incident coordination, release validation, deployment readiness assessments, and runtime reliability improvement initiatives.
Drive modernization of enterprise test automation capabilities leveraging AI-assisted testing, intelligent automation, reusable testing accelerators, scalable quality engineering frameworks, and measurable quality metrics.
Support enterprise AI observability and evaluation initiatives to improve reliability, traceability, runtime visibility, and operational confidence across AI-enabled systems.
Collaborate with Clinical, Operational, and Engineering stakeholders to support validation approaches for healthcare workflows, payer operations, and AI-enabled business processes.
Lead enterprise strategies supporting AI test data modernization, synthetic data capabilities, simulation environments, and scalable testing ecosystems enabling secure, realistic, and enterprise-grade AI validation.
Oversee development of enterprise synthetic test data platforms, healthcare simulation ecosystems, and hospital twin environments supporting AI engineering, testing, validation, and deployment readiness.
Partner with Data, Clinical, Engineering, Security, Governance, and AI teams to support scalable simulation frameworks, realistic operational testing scenarios, PHI-safe validation environments, and AI-enabled testing acceleration initiatives.
Support development of simulated healthcare operational environments and hospital twin capabilities enabling workflow validation, operational stress testing, scenario simulation, and safe evaluation of AI-enabled healthcare processes.
Drive scalable approaches for synthetic data governance, simulation fidelity, test environment automation, and secure testing ecosystem management within regulated healthcare environments.
Partner with AI Governance, Risk, Compliance, Security, Clinical, and Operational stakeholders to support responsible AI practices, operational safeguards, human oversight controls, validation traceability, auditability, and secure deployment of AI solutions.
Support development and implementation of enterprise AI quality controls, validation evidence processes, testing governance frameworks, risk mitigation practices, and quality review standards aligned with enterprise and regulatory expectations.
Help establish scalable processes supporting secure, compliant, reliable, and measurable AI solution deployment within regulated healthcare environments.
Lead AI Quality Engineering portfolio activities, including organizational planning, vendor coordination, contractor oversight, delivery prioritization, execution governance, and scalable capability development.
Partner with enterprise stakeholders to evaluate implementation readiness, quality risks, scalability considerations, testing feasibility, and delivery dependencies for prioritized AI initiatives.
Support tooling evaluations, platform assessments, automation strategies, and modernization initiatives supporting enterprise AI Quality Engineering maturity.
Define scalable squad structures, delivery engagement models, support processes, and governance approaches supporting enterprise AI transformation initiatives.
Help drive organizational capability development, workforce maturation, operating model evolution, and enterprise adoption of modern AI Quality Engineering practices.
Lead, mentor, and develop quality engineering leaders, managers, engineers, analysts, and contractor teams while fostering a collaborative, continuously learning, and engineering-driven culture.
Communicate quality risks, testing strategies, governance implications, implementation tradeoffs, and strategic recommendations effectively to both technical and executive stakeholders.
Promote a culture of engineering excellence, continuous improvement, enterprise accountability, responsible AI adoption, and operational discipline.
Research and evaluate emerging AI quality engineering, testing, observability, validation, simulation, automation, and runtime assurance technologies supporting innovation and continuous improvement initiatives.