About
The project focuses on building next-generation intelligent gaming platforms where AI enhances customer experiences, operational efficiency, and real-time decision-making. The platform ecosystem supports large-scale, highly regulated environments where reliability, scalability, and security are essential. The engineering teams are developing AI-powered solutions and agentic systems designed to automate workflows, improve platform operations, and enable trusted interactions across multiple products and jurisdictions. A strong emphasis is placed on responsible AI adoption, observability, compliance, and resilient deployment practices. The AI Quality Engineer will contribute to ensuring the reliability, safety, and performance of LLM-powered applications and AI-driven workflows operating in production environments.
Relocation package
Job rotation
Learning through Arnia Academy
Attractive projects
Flexibile working hours
Performance bonuses
Medical benefits
Trainings
Competitive compensation package
Referral program
International work experience

Requirements

  • 3+ years of experience in QA Engineering, Test Automation, or SDET roles
  • Experience testing distributed systems, APIs, or data-driven applications
  • Strong programming skills in Python or .NET
  • Hands-on experience with automated testing frameworks
  • Good understanding of CI/CD pipelines and modern development practices
  • Familiarity with microservices and integration testing
  • Exposure to AI/ML systems, NLP applications, or LLM-based workflows
  • Understanding of common LLM behaviors and failure patterns
  • Strong analytical, troubleshooting, and problem-solving skills
  • Experience working in agile, cross-functional teams
  • Good communication and documentation skills

 

Responsibilities

  • Design and implement testing strategies for AI-powered applications and agentic workflows
  • Build and maintain automated testing frameworks for LLM systems, APIs, and platform integrations
  • Validate AI outputs, agent behavior, orchestration logic, and end-to-end workflows
  • Identify edge cases, hallucinations, inconsistencies, and system failure scenarios
  • Define and monitor quality metrics such as accuracy, latency, drift, and reliability
  • Integrate automated validation into CI/CD pipelines and deployment workflows
  • Collaborate closely with AI engineers, DevOps teams, and product stakeholders
  • Contribute to observability, monitoring, and responsible AI deployment practices
  • Document testing methodologies, quality benchmarks, and validation processes
  • Stay up to date with emerging AI testing and evaluation practices

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General application

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