Quality Engineer, AI & Test Automation
Job in
Dallas, Dallas County, Texas, 75215, USA
Listed on 2026-09-20
Listing for:
Apt
Full Time
position Listed on 2026-09-20
Job specializations:
-
Quality Assurance - QA/QC
AI QA / Validation Engineer, IT QA Tester / Automation
Job Description & How to Apply Below
- The Quality Engineer, AI & Test Automation helps modernize Client’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.
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.
- 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.
- 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.
- 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.
- 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,…
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