Director Quality Engineering

JOB SUMMARY

The Director of Quality Engineering leads the transformation of BSWH’s quality assurance approach from primarily manual testing to a modern, automation-first quality engineering model. This role is accountable for establishing enterprise quality engineering practices that improve both product quality and speed of delivery across BSWH technology teams.

This leader owns quality engineering strategy, test automation, AI-enabled testing practices, quality metrics, release readiness, and the operating model for QA resources across BSWH. The role partners closely with Engineering, Product, Architecture, AI Foundation, Data & Analytics, Security, Privacy, Clinical, Operations, and vendor/partner teams to ensure that software and AI-enabled products are delivered safely, reliably, and efficiently.

The Director of Quality Engineering is responsible for building scalable quality practices that support modern engineering delivery, including shift-left testing, automated regression, API and UI automation, quality gates, performance testing, production validation, AI evaluation support, and continuous improvement. This role is a critical enabler of delivery velocity, product reliability, and customer trust.

This position can be based in our administrative building in Dallas, Texas or mostly remote with some travel required.

ESSENTIAL FUNCTIONS OF THE ROLE

Quality Engineering Strategy & Transformation
- Define and lead the enterprise quality engineering strategy for BSWH, shifting the organization from traditional manual QA toward automation-first, engineering-integrated, and AI-enabled quality practices.
- Establish a multi-year roadmap for quality modernization, including test automation, tooling, metrics, delivery integration, talent development, and operating model changes.
- Set enterprise standards for quality engineering across digital products, application teams, platform teams, AI use cases, and shared technology services.
- Create clear expectations for when testing should be automated, manually validated, embedded within engineering teams, or governed through centralized quality standards.
- Drive adoption of quality engineering practices that improve release confidence while reducing cycle time, rework, and dependency on late-stage manual testing.

Test Automation & AI-Enabled Testing
- Lead the design and implementation of scalable test automation frameworks across API, UI, integration, regression, performance, accessibility, and end-to-end testing.
- Introduce AI-enabled testing capabilities where appropriate, including test generation, test maintenance, defect analysis, intelligent regression selection, synthetic data support, and productivity acceleration for QA teams.
- Establish automation coverage targets, automation quality standards, and reporting mechanisms that make test effectiveness visible to engineering and product leadership.
- Partner with engineering teams to embed automated testing into CI/CD pipelines, release gates, and development workflows.
- Continuously evaluate tooling, frameworks, and emerging AI capabilities that can improve quality, reliability, and delivery speed.

AI Quality & Evaluation Partnership
- Partner with AI Architecture, AI Foundation, Engineering, Product, and Data teams to define quality practices for AI-enabled and agentic systems.
- Support the development of evaluation approaches for AI-enabled workflows, including expected behavior, acceptance criteria, guardrail validation, regression testing, hallucination/error detection, escalation patterns, and human-in-the-loop validation.
- Ensure AI-enabled products have appropriate quality measures for accuracy, consistency, safety, traceability, source attribution, fallback behavior, and operational readiness.
- Work with engineering and AI quality stakeholders to integrate test automation, evaluation frameworks, monitoring, and feedback loops into AI delivery practices.
- Help ensure AI use cases can scale safely without relying on one-off or purely manual validation approaches.

Release Quality, Reliability & Operational Readiness
- Define release-readiness standards, quality gates, defect triage processes, regression expectations, test evidence requirements, and production validation practices.
- Ensure teams have clear quality metrics and release…