Quality Engineer, AI & Test Automation
Baylor Scott & White Health· enterprise· Hospital & Health Care· Oct 22, 2026 · 6d ago
United States
Remote
Full-time
Staff
$147K–$218K est.
5+ years exp
Job description
JOB SUMMARY
· The Quality Engineer, AI & Test Automation helps modernize BSWH’s quality approach by moving beyond traditional manual testing toward automation-first, engineering-integrated, and AI-enabled quality practices.
· This role designs, builds, executes, and maintains automated test suites, validation utilities, evaluation assets, and quality reporting that improve release confidence and speed of delivery across digital products, enterprise applications, and AI-enabled use cases.
· The role partners closely with Product, Engineering, Architecture, Data & Analytics, AI Foundation, Ontology, Security, Privacy, Clinical, Operations, and other delivery teams to define quality expectations early and embed quality practices throughout the development lifecycle.
· This role is expected to combine strong testing discipline with technical skills, including automation, API validation, data validation, CI/CD integration, quality metrics, defect triage, and emerging AI-enabled testing practices.
· The Quality Engineer, AI & Test Automation reports to the Director of Quality Engineering and helps establish reusable practices that support safe, reliable, efficient, and scalable delivery.
ESSENTIAL FUNCTIONS OF THE ROLE
Quality Engineering & Test Strategy
· Develop test strategies, test plans, acceptance criteria, and quality approaches for software and AI-enabled capabilities in partnership with Product, Engineering, Architecture, and business stakeholders.
· Translate requirements, user stories, workflows, APIs, data dependencies, and operational expectations into clear test scenarios and quality validation plans.
· Identify quality risks early in the delivery lifecycle and recommend appropriate validation approaches, including automated, manual, exploratory, integration, regression, performance, accessibility, or AI-specific evaluation methods.
· Provide clear release-readiness input based on test evidence, defect trends, quality metrics, risk assessment, and stakeholder expectations.
Test Automation & Engineering Practices
· Design, build, maintain, and improve automated test suites for API, UI, integration, end-to-end, regression, data, and workflow validation.
· Integrate automated tests into CI/CD pipelines and delivery workflows so quality signals are available earlier and more consistently throughout the development lifecycle.
· Create reusable test data, utilities, fixtures, scripts, and automation patterns that can be used across multiple teams and products.
· Improve test reliability, maintainability, execution time, coverage, and signal-to-noise ratio by reducing brittle scripts and eliminating repetitive manual validation where practical.
AI-Enabled Testing & Evaluation
· Support testing and evaluation of AI-enabled experiences, including agent workflows, generated responses, retrieval behavior, tool/API calls, escalation paths, guardrail behavior, and human-in-the-loop patterns.
· Help define evaluation datasets, expected behaviors, failure modes, scoring rubrics, regression scenarios, and quality thresholds for AI-enabled capabilities.
· Use AI-enabled testing tools where appropriate to generate test cases, analyze logs, triage defects, identify coverage gaps, summarize results, and accelerate quality workflows.
· Partner with AI Quality, Architecture, Engineering, and Product teams to validate that AI-enabled capabilities are useful, reliable, explainable enough for the use case, and aligned to defined quality expectations.
Defect Management, Root Cause & Continuous Improvement
· Identify, document, reproduce, triage, and communicate defects with sufficient technical detail to support efficient resolution by engineering teams.
· Analyze defect patterns, escaped defects, quality trends, release issues, and production feedback to identify root causes and improvement opportunities.
· Collaborate with engineering teams to improve testability, observability, logging, error handling, and diagnostic capabilities across applications and AI-enabled workflows.
· Contribute to retrospectives, quality reviews, and process improvements that reduce rework, improve release confidence, and increase delivery velocity.
Cross-Functional Partnership & Release Readiness
· Work closely with Product Managers, Senior AI Product Managers, Software Engineers, AI Engineers, Data Product Owners, Architects, Designers, Clinical and Operational stakeholders, and vendors/partners to align on quality expectations and release readiness.
· Ensure quality criteria are embedded in requirements, design reviews, backlog refinement, development, testing, implementation, and post-release monitoring.
· Support production validation, monitoring handoffs, post-release assessment, incident follow-up, and quality reporting for assigned products and use cases.
· Communicate quality status, risks, defects, test results, and recommendations clearly to technical and non-technical stakeholders.
Quality Standards, Documentation & Enablement
· Create and maintain test documentation, automation standards, quality playbooks, release-readiness checklists, defect triage practices, and reusable testing patterns.
· Help teams adopt automation-first and AI-enabled quality practices while clarifying where manual validation remains necessary due to risk, ambiguity, or workflow complexity.
· Share knowledge with other quality, product, engineering, and business stakeholders to raise BSWH’s overall quality engineering capability.
· Stay current on modern testing approaches, automation frameworks, AI evaluation practices, test data management, observability, and quality engineering methods.
KEY SUCCESS FACTORS
· Demonstrated ability to build and maintain meaningful automated tests rather than relying primarily on late-stage manual testing.
· Strong technical judgment across API testing, UI testing, integration testing, data validation, regression testing, defect triage, CI/CD integration, and release readiness.
· Ability to test AI-enabled systems where behavior may be probabilistic, workflow-dependent, or context-sensitive rather than purely deterministic.
· Ability to use AI-enabled productivity tools responsibly to improve test generation, analysis, defect triage, and quality reporting while maintaining human judgment over release quality.
· Strong collaboration with product and engineering teams to define quality expectations early and build quality into delivery practices rather than inspect quality at the end.
· Strong analytical and communication skills, including the ability to explain quality risks, evidence, tradeoffs, and release recommendations to technical and non-technical stakeholders.
· Comfort operating in a regulated or high-trust environment where safety, privacy, security, compliance, auditability, and customer trust are critical.
BENEFITS
Our competitive benefits package includes the following
• Immediate eligibility for health and welfare benefits
• 401(k) savings plan with dollar-for-dollar match up to 5%
• Tuition Reimbursement
• PTO accrual beginning Day 1
Note: Benefits may vary based upon position type and/or level
MINIMUM REQUIREMENTS
Education
· Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or related technical field.
· Equivalent combination of education and relevant technical experience may be considered.
Experience
· 5–8+ years of experience in quality engineering, software testing, test automation, SDET, software engineering, AI quality, or related technology delivery roles.
· Hands-on experience designing and implementing automated tests for APIs, web or mobile applications, workflows, data validation, integrations, or end-to-end product experiences.
· Experience working with engineering teams in agile, DevSecOps, CI/CD, or modern product-delivery environments.
· Experience with defect triage, root-cause analysis, test evidence documentation, release readiness, and quality metrics.
· Exposure to AI-enabled products, machine learning systems, large language models, conversational experiences, agentic workflows, automation, or decision-support technologies preferred.
· Healthcare, life sciences, financial services, or other regulated-industry experience preferred but not required.
Required Technical Expertise
· Strong understanding of software testing concepts, including test design, test coverage, functional testing, regression testing, integration testing, data validation, exploratory testing, and release validation.
· Hands-on experience with test automation frameworks, scripting, test data, CI/CD integration, and defect management tools.
· Ability to validate APIs, backend services, user interfaces, data flows, workflows, and enterprise integrations.
· Programming, scripting, or data analysis skills, preferably Python, JavaScript/TypeScript, Java, SQL, or comparable languages used to build or maintain automated test assets.
· Familiarity with AI-enabled testing and evaluation concepts, including prompt/response validation, model-output evaluation, RAG or retrieval testing, agent workflow testing, hallucination risk, guardrails, and human-in-the-loop workflows.
· Ability to use logs, traces, telemetry, monitoring, dashboards, test artifacts, and production feedback to investigate quality issues.
PREFERRED QUALIFICATIONS
· Experience as a Software Development Engineer in Test (SDET), Quality Engineer, Test Automation Engineer, Software Quality Engineer, AI Quality Engineer, or similar technically oriented quality role.
· Experience building or maintaining automation frameworks using tools such as Playwright, Cypress, Selenium, Appium, PyTest, Postman/Newman, JUnit, TestNG, or comparable frameworks.
· Experience testing AI agents, copilots, chatbots, LLM-powered applications, workflow automation, RAG systems, tool-calling workflows, or other AI-enabled capabilities.
· Experience creating evaluation suites, golden datasets, scoring rubrics, regression tests, synthetic test data, or quality dashboards for AI-enabled systems.
· Experience with cloud, APIs, microservices, event-driven systems, data platforms, analytics, observability tools, or modern application architectures.
· Experience supporting quality engineering transformation, reducing manual testing dependence, or introducing AI-enabled testing practices into product teams.
Application prep
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Keywords to mirror
aiapici/cdaccessibilityagilemachine learningpythonjavascripttypescriptjavasqlllm
- Platform
- phenom
- Effort
- medium
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