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QA Engineer

Palo Alto Networks
Email

QA Engineer

 

The Team

Engineering

 

Job Summary

Palo Alto Networks NGFW (Next Generation Firewall) group is looking for an AI Automation Test engineer to contribute to networking & cloud security features. You will be part of a world-class software test engineering team that works on various ground-breaking technologies and solves interesting problems in network and cloud security. Working in a small and fast-paced team, you will solve important customer problems and deliver the most impactful results across all phases of end-to-end product and solution validation.

 

Key Responsibilities

AI-Powered Automation first Mindset: Design and execute sophisticated test automation strategies using AI-driven tools to improve test creation speed, coverage, and maintenance.

Optimize Test Suites with AI: Use AI-based analytics to identify redundant tests, predict high-risk areas, and intelligently prioritize test execution within our CI/CD pipeline.

Champion AI in QA: Serve as a champion on AI testing tools. Research, evaluate, and recommend new tools and techniques to continuously improve our QA process.

Collaborate and Integrate: Work closely with developers and DevOps engineers to seamlessly integrate AI-driven testing into the software development lifecycle (SDLC).

 

Required Qualifications

Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

1+ years of experience in Software Engineering.

At least 1 year of hands-on experience using AI-based tools for coding/testing tasks.

Ability to collaborate effectively within and across engineering teams to achieve shared quality goals.

Excellent problem-solving skills, with the ability to distill complex system issues into clear, simple, and actionable bug reports.

 

Preferred Qualifications

Demonstrated willingness to continuously learn and research innovative, AI-based testing tools and methodologies to drive Engineering productivity improvements.

Familiarity with Agentic AI architecture.

Familiarity with agentic frameworks like ADK, langchain etc.

Ability to leverage agentic AI for code generation, debugging, refactoring, and testing, as well as troubleshooting complex autonomous systems.

Able to connect AI agents with external systems, data sources, and tools to achieve complex goals.