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: MEXICO
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.