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Senior AI Quality & Reliability Engineer

Vizient · Chicago, IL · $102,400–$179,000 a year · Full time

Posted Aug 19, 2026 · We last checked this listing on Oct 5, 2026

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Likely interview questions for this role

Written from this job description, not a generic list. Each one notes what the interviewer is really checking.

About your past

Tell me about a testing framework you built from scratch. What design choices mattered most?

What they are checking: Hands-on engineering depth in building scalable, maintainable test infrastructure.

How do you mentor engineers who come from traditional QA into AI quality work?

What they are checking: Teaching new evaluation mindsets, a stated part of this senior role.

What would you do

A model update improves average quality but makes a few important cases worse. What do you recommend?

What they are checking: Weighing aggregate metrics against critical failures, which matters in healthcare workflows.

You find a quality problem late in a release cycle and the team wants to ship anyway. What do you do?

What they are checking: Communicating risk clearly and helping leaders make an informed decision rather than staying quiet.

About the work

Design an evaluation approach for an LLM feature that summarizes healthcare documents. What would you measure?

What they are checking: Defining measurable quality criteria like accuracy, completeness and hallucination rate for a sensitive use case.

How do you build and maintain a golden dataset for AI validation as the product changes?

What they are checking: Practical dataset curation, versioning and expansion from real failures over time.

What does runtime observability look like for an AI system you're responsible for?

What they are checking: Using tracing, telemetry and quality monitoring to catch problems after release, not just before.

How is reliability engineering for AI different from reliability for normal services?

What they are checking: Understanding that AI can fail silently with wrong answers, which uptime metrics won't catch.

How would you test that an AI workflow follows the actual steps of a healthcare process correctly?

What they are checking: Validating workflow logic and handoffs against real domain rules, not just output text.

The team wants to use an LLM to grade another LLM's answers. What are the risks, and how would you manage them?

What they are checking: Understanding judge-model bias and the need to calibrate automated grading against human review.

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The full job description

As published by Vizient.

When you’re the best, we’re the best. We instill an environment where employees feel engaged, satisfied and able to contribute their unique skills and talents while living and working as their authentic selves . We provide extensive opportunities for personal and professional development, building both employee competence and organizational capability to fuel exceptional performance through an inclusive environment both now and in the future. Summary In this role, you will drive the hands-on design, development, and execution of AI Quality Engineering initiatives supporting Vizient's enterprise AI transformation efforts. You will design and implement AI Quality Engineering practices, AI validation processes, AI-assisted testing approaches, runtime quality controls, and scalable testing frameworks that support the responsible deployment of AI-powered business solutions. This role combines hands-on quality engineering, AI-enabled testing modernization, healthcare workflow validation, technical mentorship, and cross-functional collaboration. You will help advance Vizient's Quality Engineering capabilities beyond traditional software testing toward AI-native validation, AI-assisted testing, runtime observability, reliability engineering, and modern AI Quality Engineering practices. Responsibilities • Design, develop, and execute AI Quality Engineering strategies supporting AI-powered applications, large language model (LLM) solutions, intelligent automation, agentic systems, and enterprise AI platforms. • Build and implement scalable AI Quality Engineering practices, including AI-native testing approaches, validation processes, runtime quality controls, reusable testing accelerators, and automated testing frameworks. • Lead AI validation activities including functional testing, prompt validation, workflow testing, regression testing, release validation, runtime quality assurance, and production reliability support. • Partner with AI Engineering, AIOps, LLMOps, Security, Governance, Clinical, Data, and Product teams to deliver scalable AI Quality Engineering processes across enterprise AI initiatives. • Support runtime reliability through observability, telemetry, distributed tracing, monitoring, drift detection, incident response, and operational quality assurance for AI-enabled systems. • Develop and maintain AI evaluation frameworks, validation datasets, quality scoring methodologies, and automated testing workflows that improve the reliability and scalability of AI solutions. • Collaborate with Clinical, Operational, and Engineering stakeholders to validate healthcare workflows, payer operations, and AI-enabled business processes while supporting responsible AI deployment through human-in-the-loop validation practices. • Coordinate testing activities across Agile delivery teams, including sprint planning, test execution, defect management, issue tracking, release readiness, risk identification, and production support. • Mentor Quality Engineers and provide technical guidance that promotes engineering excellence, AI-enabled testing modernization, continuous improvement, and adoption of modern Quality Engineering practices. • Research, evaluate, and recommend emerging AI Quality Engineering, testing automation, observability, and runtime assurance technologies to continuously improve enterprise AI quality capabilities. Qualifications • Relevant degree preferred. Advanced degree in Computer Science, Engineering, or a related field highly preferred. Bachelor's degree in Computer Science, • 5 or more years of experience in Quality Engineering, Quality Assurance, software testing, enterprise application delivery, technology operations, or related technology functions required. • 2 or more years of experience providing technical leadership for testing initiatives, automation programs, or enterprise technology delivery projects required. • Experience supporting Quality Engineering or Quality Assurance across enterprise platforms, APIs, healthcare applications, operational workflows, or integrated business systems required. • Strong knowledge of software development life cycle (SDLC), Agile methodologies, test automation frameworks, defect management, release validation, and production support processes required. • Experience validating AI-powered applications, intelligent automation, machine learning, large language model (LLM), or AI-enabled business workflows preferred. • Experience with AI Quality Engineering practices, AI-assisted testing, runtime observability, monitoring, telemetry, or reliability engineering preferred. • Strong analytical, problem-solving, organizational, communication, collaboration, and leadership skills required. • Demonstrated ability to manage multiple priorities and deliver results within fast-paced, highly collaborative enterprise environments required. • Experience in healthcare technology, payer operations, clinical workflows, or other regulated industries supporting AI governance and responsible AI deployment preferred. #LI-JB1 Estimated Hiring Range: At Vizient, we consider skills, experience, and organizational needs in our compensation approach. Geographic factors may adjust the range estimate and hires typically fall below the top range. Compensation decisions are tailored to individual circumstances. The current salary range for this role is $102,400.00 to $179,000.00. This position is also incentive eligible. Vizient has a comprehensive benefits plan! Please view our benefits here: http://www.vizientinc.com/about-us/careers Equal Opportunity Employer: Females/Minorities/Veterans/Individuals with Disabilities The Company is committed to equal employment opportunity to all employees and applicants without regard to race, religion, color, gender identity, ethnicity, age, national origin, sexual orientation, disability status, veteran status or any other category protected by applicable law.

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