N-iX is looking for a Tech Lead QA Engineer with AI experience to join our team. We're looking for a hands-on Tech Lead/Senior Manual QA Engineer who can cover the full testing spectrum: UI, API, and AI/ML output validation. You'll 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 We offer*: Flexible working format - remote, office-based or flexible A competitive salary and good

Compensation

package Personalized career growth Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more) Active tech communities with regular knowledge sharing Education reimbursement Memorable anniversary presents Corporate events and team buildings Other location-specific

Benefits

*not applicable for freelancers