Software Development Engineer in Test

About Us

Job Overview

Responsibilities

• 1.Own end-to-end product testing across mobile applications, APIs, and test automation. Leverage large language models and AI agents, such as Claude and Dify, to design and implement AI-powered solutions for test case generation, automation script development, defect diagnosis, and regression testing. 2.Lead the design and development of AI-driven testing tools and platforms, such as automated test case generation systems, intelligent defect analysis assistants, and shift-left API testing tools, to improve testing efficiency and product quality. 3.Develop a deep understanding of pain points in mobile application and API testing. Use AI agents such as Claude to optimize testing workflows, including automated regression test selection, test data generation for exception and boundary scenarios, API documentation parsing, and automated test plan generation. 4.Contribute to the continuous improvement of our test automation ecosystem by integrating AI capabilities with existing frameworks such as Pytest, Selenium, and Appium. Improve script stability and reusability while reducing manual maintenance effort. 5.Establish and document best practices for AI-assisted testing. Produce technical documentation and user guides, promote the adoption of AI tools across the team, and help improve overall quality engineering productivity. 6.Collaborate with engineering and product teams to identify risks and optimize testing strategies for complex scenarios, including high-concurrency APIs and cross-platform compatibility.

Qualifications

• 1.Bachelor’s degree or above in Computer Science, Software Engineering, or a related field, with at least three years of experience in test development or quality engineering and a strong foundation in mobile application and API testing. 2.Proven experience applying AI to improve testing efficiency. Proficiency with large language models such as Claude, GPT, or ERNIE Bot, as well as AI agent frameworks or platforms such as Dify or LangChain. Demonstrable results in areas such as test case generation, automation script development, or defect analysis are required. 3.Proficiency in at least one programming language, such as Python, Java, or Go. Familiarity with mainstream testing frameworks such as Pytest or JUnit, API testing tools such as Postman or JMeter, and automation tools such as Selenium or Appium. 4.Solid understanding of shift-left and shift-right testing practices. Ability to independently design and implement AI-powered tools or platforms that improve testing efficiency and quality. 5.Strong logical thinking and problem-solving skills, with the ability to quickly identify opportunities and pain points in AI-assisted testing and develop practical solutions. 6.Strong communication and collaboration skills, with a willingness to share knowledge, best practices, and hands-on experience in AI-powered quality engineering.