Principal/Senior QA QC Engineer (AI & Data Projects) in HCM/Hanoi

We are seeking an AI QA & Data Quality Specialist to ensure the quality, reliability, and performance of AI-driven products and the data pipelines that support them. This role focuses on validating AI model outputs, testing AI-powered features, and ensuring the integrity and quality of datasets and data pipelines used for training and inference.
You will work closely with engineering, data engineering, and product teams to improve the overall quality of AI systems through systematic testing, data validation, and automation.
This role expects a strong security mindset, with a clear understanding of data privacy, credential protection, device security, and the responsible use of corporate systems, tools, and accounts.
Key Responsibilities
AI System Quality Assurance
Design and execute test strategies for AI/ML-powered applications and features.
Validate AI model outputs for accuracy, reliability, and consistency.
Perform prompt testing, response evaluation, and edge case validation for AI systems.
Identify issues such as hallucinations, bias, incorrect reasoning, or unstable responses
Define acceptance criteria and quality benchmarks for AI-driven features.
Data Engineering & Data Pipeline Testing
Validate data pipelines (ETL/ELT) used to prepare datasets for AI models.
Test data ingestion, transformation, and loading processes to ensure reliability.
Verify data integrity between source systems, data warehouses, and AI models.
Detects and reports data anomalies, schema changes, missing data, or transformation errors.
Create data validation rules and automated data quality checks.
Dataset & Model Evaluation
Validate training datasets and feature engineering pipelines.
Monitor dataset quality to prevent data drift or unexpected data changes.
Define and track AI model evaluation metrics such as accuracy, precision, recall, and response quality.
Collaborate with data scientists to analyze model performance and identify improvement areas.
Test Automation
Develop automated tests for AI APIs, workflows, and data pipelines.
Build automation frameworks to test AI output regression and data validation.
Integrate automated testing into CI/CD pipelines.
Collaboration & Process Improvement
Work closely with AI engineers, data engineers, product managers, and QA teams.
Provide feedback during AI model development and deployment cycles.
Contribute to building AI testing standards, data quality guidelines, and QA best practices.
Required Qualifications
Bachelor's degree in Computer Science, Software Engineering, Data Science, or related field.
5+ years of experience in software QA, test automation, or data validation and 2+ years of experience in AI & Data quality testing
Experience testing APIs, web services, or distributed systems.
Strong knowledge of SQL and data validation techniques.
Strong with Python for data automation, typescript and or Java for other automation.
Understanding of machine learning concepts and AI system behavior.
Experience with test automation tools.
Experience testing AI systems, LLM applications, or chatbots.
Experience with big data platforms.
Knowledge of prompt engineering or AI evaluation methods.
Experience working with cloud platforms AWS
Familiarity with CI/CD and DevOps practices.
Generative AI: Enthusiastic user of Generative AI and advanced AI tooling to streamline workflows and significantly accelerate project delivery timelines.
Product Mindset: Proven track record to translate high-level product requirements into detailed requirements, and into comprehensive technical requirements through close partnership with Product Managers
Communication: Good verbal and written English skills, ensuring clarity and alignment within distributed, multi-national product engineering teams.
Key Skills
AI/ML testing
Data pipeline testing
Data quality validation
SQL and dataset analysis
Test automation
API testing
Prompt evaluation
Data integrity verification
Success Metrics
Improved AI output quality and reliability (accuracy rate, latency, token, cost,...)
Early detection of data pipeline or dataset issues
Increased automation coverage for AI and data testing
Reduced production issues caused by data or AI failures