Тип вакансії: Повна зайнятість

Розташування:

N-iX is looking for a Senior/Tech Lead QA Engineer with AI experience to join our team.
We're looking for a hands-on Senior/Tech Lead 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
5+ 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

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

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