Quality Assurance Engineer

The company is seeking a Quality Assurance Engineer with strong automation, performance, and API testing expertise, along with hands-on experience validating AI-enabled products and systems. The ideal candidate will combine deep QA engineering skills with modern software development practices, strong programming ability, and familiarity with AI-assisted development and AI/ML system testing.

Responsibilities:

• Design, develop, and maintain automated test suites for web, API, and distributed systems.
• Perform hands-on code writing and code reviews for test automation and quality engineering work.
• Evaluate complex user requests and clarify functional and technical requirements for new or modified applications.
• Build and execute test strategies across functional, regression, integration, performance, and end-to-end testing.
• Conduct feasibility studies for new applications and existing systems, and make sound decisions within defined parameters.
• Improve and sustain software quality using strong domain knowledge and engineering best practices.
• Collaborate across product, engineering, and platform teams to solve quality and reliability issues.
• Contribute to hiring activities, team collaboration, and knowledge sharing.
• Uphold quality standards and mentor junior team members on testing practices and engineering excellence.

Requirements:

• Strong experience in QA, SDET, or quality engineering roles.
• Hands-on experience with Playwright, Selenium, Cypress, or similar automation frameworks.
• Strong programming skills in TypeScript and/or Python.
• Experience in API testing and validating distributed systems.
• Performance testing expertise with tools such as k6 JMeter, or Locust.
• Solid understanding of the software development lifecycle and Agile methodologies.
• Experience with automated unit and integration testing tools such as JUnit, TestNG, Rest-Assured, Karate, or Cucumber.
• Familiarity with mobile testing tools such as Appium or Espresso is a plus.
• Experience with CI/CD pipelines using Jenkins or GitHub Actions.
• DevOps familiarity, including Docker and/or Kubernetes, is a strong plus.
• Strong understanding of design patterns and architectural principles for scalable, maintainable systems.

AI-Related Skills and Experience:

• This role specifically requires candidates who can support AI-driven products and workflows. Ideal candidates should have:
• Experience testing AI/ML systems, including LLMs, RAG pipelines, and agentic workflows.
• Familiarity with validating AI outputs for accuracy, consistency, safety, latency, and reliability.
• Understanding of how to test systems that use prompting, retrieval, tool use, and multi-step orchestration.
• Experience using or evaluating AI-assisted development tools such as Claude Code, GitHub Copilot Cursor, or similar tools.
• Ability to design test strategies for AI features where outputs may be probabilistic rather than deterministic.
• Comfort working with engineering teams building AI-enabled applications, copilots, or multi-agent systems.

Preferred Qualifications:

• Experience in frontend testing with React and TypeScript.
• Background in performance and capacity planning for distributed and mobile environments.
• Exposure to cloud-native engineering practices and scalable test infrastructure.
• Experience mentoring others and raising quality standards across teams.

What Success Looks Like:

• High-confidence automated coverage across critical user journeys.
• Reliable validation of AI-powered features and workflows.
• Strong collaboration with engineering teams to prevent defects early.
• Improved release quality, performance, and maintainability across products.