Senior AI QA Automation Test Engineer

The main responsibilities of the position include:

Acting as the primary technical enabler for the QA organization by building scalable AI/ML frameworks, libraries, and tooling that support broader engineering adoption
Collaborating closely with QA, Data Science, and Engineering teams to ensure seamless integration of AI-driven testing capabilities within the CI/CD ecosystem
Leading research and experimentation initiatives focused on emerging AI testing methodologies, tools, and best practices
Mentoring and supporting engineers through hands-on collaboration, code reviews, technical workshops, and architectural guidance
Designing and implementing advanced autonomous QA agents and workflows using modern AI orchestration frameworks and technologies
Building sophisticated AI evaluation pipelines to assess reasoning quality, robustness, hallucination rates, fairness, and overall model reliability
Developing resilient, AI-augmented, and self-healing automation frameworks capable of adapting to dynamic product and UI changes
Implementing machine learning-driven analytics and intelligent quality engineering solutions, including predictive quality insights, root cause analysis, and smart test prioritization 

Main requirements:

BSc/MSc in Computer Science, Artificial Intelligence, or related discipline
8+ years of hands-on experience in AQA  
1+ years of experience applying AI or ML technologies in software testing or QA process improvement  
A proven history of personally building and integrating AI/ML models into production workflows or SDLC processes
Coding proficiency in Java/Python and/or TypeScript, with a deep, practical understanding of complex software architecture and distributed system design
Hands-on experience designing and implementing complex AI agent architectures (LLM-as-a-judge, human-in-the-loop, RAG, multi-agent orchestration)
Deep architectural knowledge of modern AI/ML tooling (LLMs, vector databases, MLOps pipelines)
Strong background in integrating advanced tooling into enterprise CI/CD pipelines (GitLab, Jenkins, GitHub Actions) and containerized cloud-native environments (Docker, Kubernetes)
Exceptional ability to communicate complex technical concepts clearly, influence engineering standards without direct authority, and collaborate effectively across disciplines 

The following will be considered an advantage:

Extensive experience with autonomous QA agents and agentic orchestration frameworks in building self-evolving test suites
Expertise in high-fidelity AI evaluation pipelines and real-time observability (e.g., LangSmith, Arize) to measure probabilistic outcomes and adversarial robustness
Knowledge of AI ethics, fairness, and bias detection in model validation
Experience with gRPC, WebSockets, and HTTP/2
Familiarity with cloud-native AI solutions (AWS Bedrock, GCP Vertex AI, Azure AI) 

Benefit from:

• Attractive remuneration package
• Intellectually stimulating work environment
• Continuous personal development and international training opportunities

The Hiring Experience: What Awaits You

• Let’s Connect – Intro Chat with Talent Acquisition
• Deep Dive – First Interview with Your Future Team
• Final Connection – Final Interview