AI Assurance Quality Engineer

We are building and scaling AI-enabled digital products that integrate Large Language Models (LLMs), data pipelines, and modern cloud-native services. This role offers the opportunity to embed AI assurance practices early in the product lifecycle, ensuring AI features are reliable, measurable, and responsibly governed before reaching production.

As an AI Assurance Quality Engineer, you will support the validation of AI-generated outputs, structured and unstructured data flows, and automation frameworks. You will work closely with engineering, data, and product teams to operationalize repeatable AI testing practices and contribute to responsible AI delivery.

This is an entry-level growth role within a modern AI engineering environment, combining software testing, data validation, and AI evaluation techniques.

• Execute test cases to validate AI-generated outputs (accuracy, relevance, hallucination detection, bias indicators).
• Support testing of Retrieval-Augmented Generation (RAG) workflows and prompt templates.
• Perform API and data validation testing (JSON, structured datasets, model responses).
• Contribute to automated regression test scripts for AI-enabled features.
• Document defects, AI quality risks, and contribute to governance evidence trail.
• Participate in sprint ceremonies, test planning, and cross-functional collaboration.
• Support accessibility and usability validation for AI-driven interface.

• Strong understanding of software testing fundamentals (functional and non-functional testing).
• Good knowledge of API testing and data validation techniques.
• Basic understanding of AI/LLM concepts and prompt-based systems.
• Strong analytical skills with attention to detail when reviewing AI output.
• Good written communication skills for documenting defects and risk.
• Familiarity with Git version control workflow.
• Certified in Azure
  
  A.

• Exposure to automation frameworks (Playwright, Cypress or similar).
• Understanding of Retrieval-Augmented Generation (RAG) concept.
• Familiarity with cloud environments (AWS, Azure, or GCP).
• Awareness of Responsible AI principles (bias detection, explainability, fairness).
• Experience testing data-driven or ML-enabled systems.
• Understanding of CI/CD pipelines and Dev Ops practice.

• Degree in Computer Science, Software Engineering, Data Science, or related discipline; or equivalent practical experience.
• Demonstrated interest in AI systems, data quality, or automation testing.