Responsibilities
Build, hire, and grow a QA/SDET team in Vietnam, and foster a strong quality culture across engineering.
Define and drive the department's QA strategy with AI at its core, aligned with company-wide quality initiatives led by the CQO, and set standard processes for testing, defect management, and release decisions.
Partner with Engineering, Product, and DevOps to shift quality left, and act as the quality owner in planning and release discussions.
Lead AI QA Initiatives & Develop AI QA Tools
Lead AI adoption in QA: evaluate AI/LLM tools and drive AI-driven practices such as test generation, self-healing tests, bug triaging, root cause analysis, and synthetic test data.
Design and build in-house AI QA tools and agents (e.g., reusable AI skills/workflows) that fit into the team's daily testing and CI/CD pipelines.
Set up a framework and metrics to measure the impact of AI in QA (e.g., time saved, test coverage, defect detection rate, AI output accuracy, adoption rate), and use the data to keep improving.
Automation Architecture & Quality Engineering
Architect a scalable, reliable test automation infrastructure, including non-functional testing (performance, load, API security), and enforce coding standards through code reviews.
Stay hands-on as the technical lead for SDETs: write and review automation code, set the framework design and best practices, and coach engineers on
software engineering skills in testing.
Own test pipeline health in CI/CD: optimize execution speed (parallelization, test selection), reduce flaky tests, and manage test environments and test data for reliable, fast feedback.
Requirements
Must-have
7+ years in QA, including 3+ years leading a QA team of 5+ members, with proven ownership of QA strategy and release decisions.
Hands-on experience building scalable test automation frameworks (Web, Mobile, or API) with tools like Playwright, Selenium, Cypress, or Appium, and integrating them into CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins).
Proven experience leading AI adoption in testing: applying GenAI/LLM tools (e.g., GitHub Copilot, Cursor, Claude, ChatGPT) and building AI-powered QA tools or workflows (prompt engineering, AI agents, LLM API integration).
Data-driven mindset: experience defining quality and AI effectiveness metrics, and communicating insights to stakeholders.
Strong proficiency in at least one programming language (TypeScript, Java, Python, or C#) and a solid understanding of system architecture to assess quality risks.
Bachelor's degree in Computer Science, IT, or a related field (or equivalent practical experience).
Business-level English and excellent communication skills, comfortable working with teams in Japan and Vietnam.
Nice-to-have
AI evaluation skills: experience evaluating LLM/AI output quality (e.g., building eval datasets, LLM-as-a-judge, eval frameworks like Promptfoo, DeepEval, or Ragas) and testing AI-powered products for accuracy, hallucination, and safety.
Experience with test management and observability tools (e.g., TestRail, Zephyr Scale, Grafana, Datadog).
Experience with Docker/Kubernetes for test environments and performance testing tools (e.g., JMeter, k6, Locust).
Contributions to open-source testing projects, technical blogs, or QA/SDET community events.