Quality Assurance (QA) Engineer (AI & Insurance Core Systems)

● Responsible for functional, regression, and system integration testing of traditional software products such as insurance core systems, accounts management, and core API services.

● Deeply understand complex insurance business logic and rules, design comprehensive test cases, and ensure the accuracy of business processes and data consistency.

● Formulate and execute efficient and rigorous testing strategies for systems that are still primarily developed using traditional software engineering methods in the medium to long term, guaranteeing stable operation in the production environment.

● Responsible for formulating and executing end-to-end testing strategies for LLM applications (e.g., RAG/Retrieval-Augmented Generation, intelligent customer service, automated underwriting tools).

● Design and execute evaluation tests for the LLM output's factuality, relevance, consistency, harmlessness, and bias, ensuring the fairness and ethics of AI decisions.

● Perform Prompt Engineering testing to validate the impact of different prompt templates and parameters on the quality of model output, and participate in optimization.

● Utilize mainstream programming languages (e.g., Python, JavaScript, Java) to develop and maintain efficient backend API automation testing frameworks (e.g., Pytest, Postman), covering interfaces for traditional software systems and AI services.

● Build and optimize Web/Mobile end-to-end automation test scripts (e.g., Selenium, Cypress, Appium) and integrate them into the CI/CD pipeline.

● Ensure the quality of datasets used for model training, fine-tuning, and RAG retrieval. Responsible for testing data extraction, cleaning, and loading (ETL pipelines).

● Participate in monitoring the stability of AI/MLOps pipelines and testing for continuous model degradation (Drift).

● Serve as an embedded QA in Agile (Scrum) teams, practicing the Shift-Left principle by engaging early in requirements analysis and design.

● Execute load, stress, and scalability testing to ensure stable system operation under high concurrency.

● Maintain and optimize the testing environment, test case repository, and defect tracking system.

1. Education Background: Bachelor's degree or above in Computer Science, Software Engineering, Mathematics, or a related field.

2. Professional Experience: 5+ years of experience in Software QA or Automation Testing, with at least 3 years of in-depth testing experience in core business systems (e.g., finance, insurance, e-commerce).

3. Technical Skills: Proficient in at least one mainstream programming language for automation test development. Possess solid knowledge of SQL databases and the ability to independently perform data validation and construction.

4. AI/Data Literacy:

○ Must understand the basic working principles of Large Language Models (LLM), RAG, and Data Pipelines.

○ Experience in engaging with or testing AI-driven products (e.g., testing model prediction accuracy, recall rate, and other metrics).

5. Tool Proficiency: Familiar with API testing tools (e.g., Postman, cURL), version control tools (Git), and CI/CD tools (e.g., Jenkins, GitLab CI).

6. Language Skills: Excellent written and verbal communication skills in Chinese; good English reading ability, capable of effortlessly reading English technical documentation.

1. Industry Experience: Relevant work experience in the FinTech or InsurTech domains.

2. Cross-border Collaboration Experience: Experience collaborating with teams in Singapore, India, or Southeast Asia, and understanding the challenges of cross-cultural communication.

3. Specific AI Testing Experience: Practical experience with testing LLM Hallucination, Security, and Explainability.

4. Familiarity with the basic knowledge of NoSQL databases (e.g., MongoDB) and mainstream cloud computing platforms (e.g., AWS, GCP).

5. Fluent English oral communication skills.