AI QA Specialist

About this role

Batch: 2024/2025/2026. Must have skills: - Strong hands-on experience with Selenium WebDriver, Playwright, and TestNG/JUnit is essential because this role expects end-to-end automation ownership. - Solid knowledge of REST Assured, Postman, and API validation is a must, since the platform is built on microservices and AI-integrated services. - Experience with Apache JMeter and understanding response times, throughput, SLA validation, and bottleneck analysis is very important. - Good coding ability in Java is critical, with added value from Python and TypeScript/JavaScript for automation and test utilities. - Since this is an AI-native platform, understanding LLMs, AI agents, non-deterministic outputs, guardrail testing, hallucination detection, and AI API testing is one of the biggest differentiators. - Knowledge of Git, microservices, REST/Kafka communication, and SQL basics is necessary to test reliably in a real engineering environment. Good to have skills: - Knowledge of Jenkins and GitLab CI/CD is very valuable because QA automation is much stronger when it is fully tied into build and deployment pipelines. - Being able to run tests in containers and set up stable test environments is a big plus, especially in modern microservices-based products. - Since the platform uses event-driven workflows, understanding how to test Kafka message production and consumption is highly useful. - Experience with Cucumber/Gherkin or Robot Framework helps in writing clear, behavior-driven scenarios that improve collaboration between QA, developers, and product teams. - Familiarity with Axe-core/WCAG validation and OWASP ZAP adds strong value because it improves both usability and product safety. - Experience with tools like Claude Code, GitHub Copilot, Cursor, or Cody is a strong advantage for speeding up test automation and working efficiently in an AI-heavy engineering setup. - Modern code-first performance testing tools are highly valuable because they fit well into CI/CD workflows and are more flexible for engineering teams than traditional load-testing setups. - This is especially important in microservices-based systems because it helps validate API boundaries and prevents service-to-service integration issues early. - A very strong plus because it enables isolated, Docker-backed test environments, making integration testing more reliable and production-like. - A useful frontend-focused E2E testing tool with a strong developer experience, especially valuable for faster debugging and tighter collaboration with frontend teams. - These are great to have because shift-left security scanning improves product quality early in the pipeline and reduces risk before release. - This stands out because observability-driven testing is becoming very important in modern distributed systems, especially for tracing failures across services.