Job Description

QA Software Test Automation Engineer - AI

The main responsibilities of the position include:

• Drive experimentation and implementation of AI and GenAI technologies in QA processes to enable intelligent test generation, prioritization, and maintenance
• Research, evaluate, and integrate AI-powered testing tools and frameworks (e.g., Copilot, TestGPT, Diffblue, or custom LLM-based solutions)
• Develop AI-augmented test automation frameworks capable of adapting to frequent product and UI changes (self-healing tests, smart locators)
• Implement AI-based analytics for root cause analysis, defect prediction, and quality risk assessment
• Define and refine strategies for testing AI-based solutions, including LLM-driven systems, agents, and ML models
• Design evaluation pipelines for AI components - measuring accuracy, robustness, fairness, explainability, and safety
• Collaborate with engineering, data science, and AI teams to integrate AI testing and AI-in-testing capabilities into CI/CD pipelines
• Stay at the forefront of emerging AI testing practices, driving innovation, experimentation, and education across QA teams
• Provide guidance and training to testers and engineers on how to leverage AI effectively in testing workflows

Main requirements:

• BSc/MSc in Computer Science, Artificial Intelligence, or related discipline
• 5+ years of hands-on experience in AQA
• 1+ years of experience applying AI or ML technologies in software testing or QA process improvement
• Proficiency in Python or JavaScript/TypeScript, with strong scripting and automation skills
• Experience with modern test automation frameworks (e.g., Playwright or similar)
• Familiarity with AI/ML workflows and tools (LLMs, vector databases, MLOps pipelines)
• Experience working in Docker/Kubernetes environments
• Understanding of AI-assisted testing approaches, such as:

o    NLP-based test case generation
o    Intelligent defect classification
o    Model-based testing using AI
o    Predictive quality analytics
• Familiarity with AI agent evaluation patterns (LLM-as-a-judge, human-in-the-loop)
• Proficiency with monitoring/observability tools such as Grafana, Prometheus, etc.
• Strong experience integrating tests into CI/CD pipelines (GitLab, Jenkins) and containerized environments (Docker, Kubernetes)

The following will be considered an advantage:

• Experience with gRPC, WebSockets, and HTTP/2
• Hands-on experience with AI-assisted test tools or frameworks (e.g., Testim, Mabl, Applitools, or custom GenAI-powered test assistants).
• Exposure to LLM-based system validation and AI observability frameworks (e.g., LangFuse, DeepEval, Trulens).
• Knowledge of AI ethics, fairness, and bias detection in model validation.
• Familiarity with cloud-native AI solutions (AWS, GCP, Azure). 

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