Senior QA Engineer with AI experience

N-iXis looking for a Senior QA Engineer with AI experience to join our team.

Were looking for a hands-on Senior Manual QA Engineer who can cover the full testing spectrum: UI API and AI/ML output validation. Youll be joining a project building an AI-powered document analysis platform from the ground up. The system combines a CMS backend modern UI and a RAG-based AI layer used for intelligent document processing and analysis.

Key Responsibilities

• Follow a phased QA approach begin with CMS and backend testing to establish a reliable baseline of expected system behavior then apply those insights to validate AI agent outputs effectively
• Design and execute test cases for REST APIs covering functional correctness edge cases error handling authentication and data integrity
• Perform UI testing across core user journeys validating layout behavior and integration with backend services
• Transition into AI output validation once the deterministic layers are stable using your knowledge of business rules to identify inconsistencies hallucinations or degraded outputs in agent responses
• Document and maintain test cases test plans and bug reports in a structured and traceable way
• Participate in requirement reviews and technical discussions to identify testability gaps early
• Collaborate with the Lead Big Data/AI Engineer and AI team to understand RAG pipeline behavior document ingestion flows and output quality expectations
• Contribute to building reusable test assets and QA processes as the project scales

Requirements

• 4 years of experience in manual QA with exposure to frontend backend and AI testing
• Solid experience with API testing using tools such as Postman REST Client or similar
• Experience testing web UIs and understanding of cross-browser/cross-environment considerations
• Ability to read and interpret API documentation data schemas and system architecture descriptions
• Hands-on experience working on projects that included AI ML or NLP components particularly validating outputs that are probabilistic or context-dependent
• Familiarity with the concept of RAG (Retrieval-Augmented Generation) or LLM-based systems
• Strong analytical thinking especially the ability to assess whether an AI response is contextually correct not just technically non-null
• Good understanding of test documentation practices: test plans test cases bug reports traceability
• English level at least Upper-Intermediate