Position Summary

We are seeking a QA Analyst to support testing across multiple products and release cycles through a combination of AI-assisted, automated, and manual testing. This role will use AI tools and agentic workflows to enhance test design, automation development, results analysis, and documentation, while applying professional judgment to validate outputs and identify risks. The ideal candidate brings foundational automation and scripting skills, strong analytical abilities, and a collaborative approach to delivering reliable results within project timelines.

Key Responsibilities

• Incorporate AI tools and agentic workflows into test design, data generation, automation development, results analysis, and documentation.
• Evaluate AI-generated outputs, identify inaccuracies or coverage gaps, and perform additional validation when necessary.
• Determine the appropriate combination of AI-assisted, automated, and manual testing based on risk, complexity, and release priorities.
• Develop, execute, and maintain automated tests using Playwright, Selenium, or comparable frameworks.
• Create and maintain automation scripts using JavaScript, TypeScript, Python, C#, or similar languages.
• Document and communicate test outcomes, escalate quality concerns, and contribute to release-readiness decisions.
• Collaborate with QA engineers, developers, business analysts, and other stakeholders to clarify requirements, resolve defects, and improve testing processes.
• Support multiple products and releases while balancing testing coverage, efficiency, risk, and quality.

Required Qualifications

• 1–3 years of software quality assurance experience, including manual and automated testing.
• Experience with, or demonstrated aptitude for, using AI tools and agentic workflows in software testing.
• Familiarity with Playwright, Selenium, or comparable test-automation frameworks.
• Basic scripting or programming skills in JavaScript, TypeScript, Python, C#, or a similar language.
• Strong analytical and investigative skills, with the ability to identify defects, coverage gaps, regression risks, and unreliable AI-generated outputs.
• Ability to select appropriate testing methods based on product risk, requirements, and project priorities.
• Effective communication and collaboration skills, including the ability to clarify requirements and raise quality concerns.
• Ability to learn product and business processes, adapt to changing priorities, and contribute across multiple products and release cycles.