Remote QAE/DevOps Architect: Azure CI/CD & AI QA

GEHA Health; Missouri; Hybrid; USD 123,000 - 156,000; Full time

Benefits offered by this job

Health/Vision/Dental benefits

401(k) retirement plan matching

Paid Time Off

Hybrid/remote work options

Job summary

GEHA Health seeks a seasoned QAE/DevOps Solutions Architect to define end-to-end SDLC and QA architecture with a strong emphasis on Azure DevOps pipelines, CI/CD integration, governance, quality gates, and AI-assisted quality engineering practices.

The role leads the evolution from manual testing to a Quality Assurance Engineering function using data to drive enterprise outcomes and mentors teams across DevOps and QA disciplines.

Qualifications

• 10+ years in QA, quality engineering, or test automation with architect experience.
• Proven lead in QA automation transformations from manual to automated.
• Deep expertise in Azure DevOps with pipelines and governance.

Responsibilities

• Define QAE/DevOps automation architecture and reference stack.
• Design, implement, and optimize Azure DevOps CI/CD pipelines with quality gates.
• Integrate automated testing (UI, API, performance, data validation) into pipelines.
• Mentor QA and DevOps engineers on best practices and governance.
• Drive continuous improvement of deployment reliability, speed, and quality metrics.
• Lead AI-assisted QA tool adoption and governance across teams.
• Collaborate with platform, engineering, and DevOps teams for end-to-end SDLC integration.

Skills

QA/Quality engineering leadership

Azure DevOps

CI/CD pipelines

Automation frameworks

Scripting (C#, Python, PowerShell)

Test automation strategy

AI-assisted QA

Governance and standards

DevOps collaboration

Data-driven QA

Education

Bachelor's or higher in a relevant field

Tools

Playwright

Selenium

Azure DevOps Pipelines

GitHub Copilot or AI tooling

Job description

GEHA Health seeks a seasoned QAE/DevOps Solutions Architect to define end-to-end SDLC and QA architecture with a strong emphasis on Azure DevOps pipelines, CI/CD integration, governance, quality gates, and AI-assisted quality engineering practices.

The role leads the evolution from manual testing to a Quality Assurance Engineering function using data to drive enterprise outcomes and mentors teams across DevOps and QA disciplines.