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.

Experience
3-7 yrs