QAE​/DevOps Solutions Architect

About the Company

Government Employees Health Association, Inc. (G.E.H.A) is a nonprofit member association that provides health and dental benefits that millions of federal employees and retirees, military retirees and their families have counted on since 1937. Offering one of the largest health and dental benefit provider networks available to federal employees in the United States, G.E.H.A empowers health and wellness by meeting its members where they are, when they need care.

G.E.H.A has one mission:
To empower federal workers to be healthy and well.

The QAE/Dev Ops Solutions Architect leads the design and implementation of enterprise-scale quality engineering, automation, and Dev Ops capabilities. This role will define end-to-end SDLC and QA architecture with a strong emphasis on Azure Dev Ops pipelines, CI/CD integration, governance, quality gates, and AI-assisted quality engineering practices. The QAE/Dev Ops Solutions Architect leads the evolution from manual testing to a Quality Assurance Engineering (QAE) function using data to drive to enterprise outcomes.

• Define QAE and Dev Ops automation architecture.
• Define and evolve the test technology stack, document the reference architecture, code standards, and repo structure the team builds against.
• Stay up to date on emerging technology in the Quality and Dev Ops space.
• Assess how new technologies can be leveraged to enhance quality services.
• Stay up to date on the quality implications for emerging technology in development, data, and analytical domains.
• Help lead and assist with the modernization of the end-to-end SDLC process and ensure our process and supporting technology stack aligns with emerging best practices.
• Design, implement, and optimize Azure Dev Ops CI/CD pipelines with integrated quality gates and governance controls.
• Establish enterprise standards for build, release, and deployment pipelines, including compliance and auditability.
• Integrate automated testing frameworks into CI/CD pipelines (UI, API, performance, data validation).
• Define and enforce QA governance models, including standards, controls, and best practices across teams.
• Participate in the automation backlog, consuming risk-ranked regression suites with a deliberate record of which test cases are automated and why.
• Identify and retire technical debt in the QA technology stack.
• Lead adoption of AI-assisted QA tools (e.g., test generation, defect prediction, intelligent automation).
• Partner with Dev Ops, engineering, and platform teams to ensure seamless end-to-end SDLC integration.
• Lead shift-left quality initiatives to embed testing early in the development lifecycle and shift-right processes to ensure learnings with Production incidents.
• Evaluate and select tools and frameworks for QA automation, pipeline optimization, and AI integration.
• Mentor QA and Dev Ops engineers on best practices for automation, pipelines, and governance.
• Drive continuous improvement of deployment reliability, speed, and quality metrics.
• Extend QA oversight onto AI agents and bot interactions, define rules/grades for bots and ensure parity of quality controls across human and virtual agents.

• 10+ years of experience in QA, quality engineering, or test automation, including previous experience in an architect role.
• Previous experience leading QA manual-to-automation transformation
• Deep expertise in Azure Dev Ops (Platform Administration, Pipelines, Repos, Test Plans, Artifacts) with a strong focus on deployment pipelines.
• Proven experience designing and governing enterprise CI/CD pipelines.
• Strong experience with automation frameworks (Playwright, Selenium, or similar).
• Experience with API testing, data validation, and pipeline-integrated testing strategies.
• Strong scripting/programming skills (C#, Python, Power Shell, JMeter, or similar).
• Experience integrating QA into CI/CD workflows with enforceable quality gates, including regression, integration, performance, and other quality checks.
• Experience establishing governance frameworks for QA and Dev Ops practices.
• Strong understanding of modern SDLC, Dev Ops, and Agile practices.
• Data and analytics literacy to design QAE processes and interpret trends.

• Experience with infrastructure-as-code (Bicep, Terraform, ARM).
• Experience with containerization (Docker, Kubernetes).
• Experience with cloud-native architectures (Azure preferred).
• Experience with AI-assisted development or testing tools (e.g.,…