QA Software Test Automation Engineer - AI
The Role:
We are looking for a QA Software Test Automation Engineer - AI to join our cutting-edge engineering organization and lead the adoption of AI technologies in software testing. This role is pivotal in transforming how we validate quality, performance, and reliability across our platforms by introducing AI-driven testing practices, while also investigating and standardizing methods for testing AI-based solutions themselves.
You will be responsible for driving AI-assisted quality engineering, developing intelligent test automation frameworks, and exploring the application of machine learning and generative AI for test generation, defect detection, and analysis. At the same time, you will evaluate and enhance the reliability of AI systems, ensuring they meet high standards for accuracy, fairness, and robustness.
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
