SDET Architect ( AI Focus )

Nexaminds is looking for a SDET Architect with strong AI/LLM exposure to lead and evolve our quality engineering practices. This role operates at an Architect / Principal level, combining deep technical expertise in test automation with the ability to drive AI adoption within QA workflows.

Location: CANADA

Qualifications we are looking for:

• 10+ years of experience in Test Automation and Quality Engineering
• Proven experience operating at Architect or Principal level
• Hands-on experience applying AI in Quality Assurance, including automation with Playwright or Selenium leveraging AI-assisted layers such as Copilot, LLMs, or similar. (E.g., test generation, self-healing, debugging, intelligent test data creation and defect analysis.)
• Strong hands-on programming expertise (Java ecosystem preferred)
• Extensive experience building automation frameworks for:
• UI automation (Selenium, Playwright)
• API automation (Postman, RestAssured, Swagger/OpenAPI)
• Integration testing
• Data validation / ETL testing
• Strong experience integrating automated testing into CI/CD pipelines (Jenkins, GitLab CI, Docker, Kubernetes)
• Deep understanding of DevOps practices and release governance
• Experience leading QA strategy for enterprise-scale platforms
• Strong communication and stakeholder management skills
• Advanced English proficiency (written and verbal)

Job duties:

• Define and lead enterprise test automation strategy across UI, API, integration, performance, and data layers.
• Ensure responsible and governed adoption of AI tools within engineering workflows.
• Architect scalable and maintainable automation frameworks using Java-based ecosystems.
• Establish and enforce CI/CD quality gates integrated into DevOps pipelines.
• Drive data quality validation strategy for ETL and large-scale data platforms.
• Lead QA transformation initiatives to improve quality maturity, automation coverage, and delivery predictability.
• Mentor QA engineers and SDETs, establishing standards, tooling, and governance models.
• Collaborate cross-functionally with Engineering, DevOps, Data Engineering, and Product teams.
• Identify, evaluate, and implement AI-enabled capabilities within QA processes.
• Continuously assess and evolve Quality Engineering best practices across the organization.