Lead QA Engineer – AI & Data Quality (ARM)

About the role

We are looking for a Lead QA Engineer to ensure the quality, reliability, and performance of AI‑driven products and the data pipelines that support them. The role focuses on validating AI model outputs, testing AI‑powered features, and safeguarding the integrity of datasets used for training and inference.

Key responsibilities

• Design and execute test strategies for AI/ML applications and features.
• Validate AI model outputs for accuracy, reliability, and consistency; perform prompt testing and edge‑case validation.
• Identify issues such as hallucinations, bias, incorrect reasoning, or unstable responses.
• Validate ETL/ELT data pipelines, test ingestion, transformation, and loading processes, and verify data integrity across source systems and AI models.
• Create automated data validation rules and quality checks; monitor dataset quality to prevent data drift.
• Define and track model evaluation metrics (accuracy, precision, recall, response quality) in collaboration with data scientists.
• Develop automated tests for AI APIs, workflows, and data pipelines and integrate them into CI/CD pipelines.
• Collaborate with AI engineers, data engineers, product managers, and QA teams to establish testing standards and best practices.

Required profile

• Bachelor’s degree in Computer Science, Software Engineering, Data Science or a related field.
• 5+ years of experience in software QA, test automation, or data validation.
• 2+ years of experience testing AI/ML systems, LLM applications, or chatbots.
• Strong knowledge of SQL and data validation techniques.
• Proficiency in Python for data automation and Typescript or Java for broader automation.
• Understanding of machine‑learning concepts and AI system behavior.
• Experience with cloud platforms (AWS) and CI/CD/DevOps practices.

Required skills

• Python
• Typescript
• Java
• SQL
• AWS
• CI/CD
• DevOps
• Test automation tools
• Big data platforms
• Prompt engineering
• AI evaluation methods