Senior Full-stack QA Engineer (AI Platform)

JetBrains is building an AI-native platform for software development that connects developer workflows, team-level collaboration, and organizational control into a single coherent system. This platform serves as the execution and governance layer for AI-driven development, integrated with developer tools and designed to work across teams, products, and environments. This is a long-term strategic investment and a key pillar of JetBrains' vision for the future of software development.

• At least three years of experience in software testing (manual and automated).
• Solid understanding of REST APIs, request/response structures, status codes, and typical scenarios.
• Hands-on experience with Postman or similar API testing tools.
• Experience writing automated tests for APIs using any programming language or framework.
• Understanding of distributed systems and microservice architectures.
• Basic Docker skills (running containers, working with Docker Compose).
• Comfortable reading and interpreting OpenAPI/Swagger specifications.
• Basic understanding of authentication and authorization protocols (e.g. OAuth 2.0, API keys, JWT).
• Ability to work independently and within a team with strong self-management skills.
• Good command of English, both spoken and written.

Nice to have:

• Hands-on experience with cloud computing platforms (AWS, GCP) and container orchestration tools (Kubernetes, Helm).
• Experience with CI/CD tools (e.g. TeamCity or GitHub Actions).
• Experience with performance testing tools (e.g. k6).
• Familiarity with TypeScript and/or JavaScript.
• Experience with automated testing infrastructure and processes.
• Enjoy digging into logs and debugging distributed systems.

• Developing and maintaining an automated testing infrastructure, setting up CI/CD pipelines, selecting and integrating existing tools, and designing new ones.
• Writing and improving automated tests, particularly for APIs.
• Collaborating with the QA and Development teams to design and execute test scenarios.
• Working with logs and monitoring tools (e.g. Kibana, Grafana) to detect functional issues and performance anomalies.
• Ensuring the quality of distributed AI-powered systems by validating integrations with various language models and APIs.

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