047 - QA Engineer – AI Maintenance & Technology Upgrades
RiDiK (a Subsidiary of CLPS. Nasdaq: CLPS) 047 - QA Engineer – AI Maintenance & Technology Upgrades
Summary
QA engineer ensuring the quality, reliability, and stability of the company's Group Ops AI services: designing, executing, and automating test plans, validating AI model performance during upgrades, and supporting production releases. Core tech includes Postman/Swagger API testing, SQL, Python, Selenium, Playwright, and CI/CD practices.
We are seeking a highly motivated QA Engineer to support the maintenance and continuous improvement of our Group Ops AI services. The role will focus on ensuring the quality, reliability, and stability of AI-powered applications during ongoing model upgrades, technology upgrades, platform enhancements, and production releases.
The successful candidate will work closely with AI Engineers, Data team, and our Technology partners to design, execute, and automate testing activities across the AI service lifecycle. This includes validating model performance, conducting regression testing, supporting release activities, and ensuring that upgrades do not negatively impact business processes or user experience.
Key Responsibilities:
Quality Assurance & Testing
Design, develop, and execute test plans, test cases, and test scripts for AI-powered applications and services.
Perform functional, integration, system, regression, and user acceptance testing (UAT) support.
Validate end-to-end workflows across AI applications, APIs, and integrated systems.
Identify, document, track, and verify resolution of defects and issues.
Model Upgrade Validation
Support testing and validation activities during AI model upgrades.
Conduct model output comparisons and regression testing to assess the impact of model changes.
Validate model performance against predefined quality metrics such as accuracy, relevance, consistency, and completeness.
Technology Upgrade & Release Support
Support testing for AI models during infrastructure, platform, security, and technology upgrades.
Validate system stability following upgrades to application frameworks, libraries, APIs, and supporting technologies.
Participate in release readiness reviews, production deployment validation, and post-release verification.
Ensure appropriate test evidence and documentation are maintained for audit and governance purposes.
Operational Support
Support production incident investigations related to model or technology changes.
Assist with root cause analysis and validation of fixes.
Monitor service quality trends and provide recommendations for quality improvements.
Work closely with Group Operations and Technology teams to ensure service reliability and business continuity.
Requirements:
Education & Experience
Bachelor's degree in Computer Science, Information Technology, Engineering, or a related discipline.
6-9 years of experience in software quality assurance, testing, or quality engineering.
Experience supporting enterprise applications, AI/ML solutions, or digital platforms is preferred.
Experience in banking, financial services, or operations environments is advantageous.
Technical Skills
Strong understanding of software testing methodologies and SDLC/STLC processes.
Experience with API testing tools such as Postman, Swagger, or equivalent.
Familiarity with SQL and data validation techniques.
Knowledge of test automation frameworks and scripting languages (e.g., Python, Selenium, Playwright).
Understanding of CI/CD pipelines and DevOps practices.
Familiarity with AI/ML or GenAI concepts, model testing, and prompt validation.
Exposure to monitoring, logging, and observability tools is a plus.
Functional Competencies
Strong analytical and problem-solving skills.
Ability to interpret test results and identify root causes of issues.
Excellent attention to detail and commitment to quality.
Strong communication and stakeholder management skills.
Ability to work independently in a fast-paced and evolving environment.
Preferred Skills
Experience testing AI, GenAI, chatbot, or machine learning solutions.
Experience validating model performance and conducting AI regression testing.
Knowledge of model governance, risk controls, and change management processes.