Senior Automation Engineer(QA)

Palo Alto Networks•Santa Clara, CA

3d•$106,000 - $170,500•Onsite

About The Position

As a Senior Software Engineer in IT Quality Engineering Automation, you will be the technical anchor and visionary for our automated testing and next-generation SDLC ecosystem, operating directly from our Santa Clara office. At Palo Alto Networks, we are transitioning from traditional QA to an AI-First engineering culture. This role is highly strategic, focusing on the complex, end-to-end (E2E) automation and integration across Palo Alto Networks' core Enterprise IT applications: Salesforce (SFDC) for Go-To-Market (GTM), SAP for Finance, and our proprietary, Custom Microservices-based platforms for Licensing and Entitlement Management. Moving beyond siloed testing, you will architect an intelligent, AI-augmented testing infrastructure that binds these critical domains together, ensuring flawless business workflows at scale.

Requirements

4+ years of professional experience in software engineering, test automation, or DevOps roles.
Deep hands-on experience and fluency with modern AI-assisted development tools (e.g., Cursor, Claude Code, GitHub Copilot Workspace, OpenAI API) and a strong understanding of how to embed them into daily workflows.
Deep proficiency in programming languages such as Python, Java, or JavaScript. Excellent software design and coding skills are paramount.
Proven experience designing and architecting frameworks using modern enterprise tools (e.g., Playwright, Cypress, Selenium, RestAssured, Karate, or PyTest).
Deep expertise integrating automated testing pipelines into enterprise CI/CD workflows (GitHub Actions, GitLab CI, or Jenkins) and managing version control (Git) at scale.

Nice To Haves

Prior experience and strong domain knowledge in any of the following core areas: Salesforce (SFDC) ecosystem supporting Go-To-Market (GTM) functions, SAP systems driving core Finance applications, Custom Microservices architectures and distributed systems (specifically focused on high-throughput transactions like Licensing and Entitlement Management).
A proven potential and appetite to quickly scale technically across unfamiliar functional domains to bridge automation gaps.
Experience building or fine-tuning custom AI tooling, prompt engineering workflows, or utilizing LLM orchestrators (e.g., LangChain, LlamaIndex).
B.E./B.Tech/M.Tech in Computer Science, Computer Engineering, or a related technical field (or equivalent practical experience).
A forward-thinking approach to engineering that defaults to using, building, and optimizing with artificial intelligence to solve complex enterprise scalability problems.
Exceptional communication skills with the ability to influence technical and non-technical stakeholders, driving cultural shifts toward AI adoption across global teams.
Strong ownership and accountability; a proven track record of taking abstract, complex integration challenges and turning them into scalable, automated technical solutions.

Responsibilities

Design, architect, and execute complex, end-to-end test automation strategies that span across diverse platforms—validating seamless data flows from SFDC (GTM) to Custom Microservices (Licensing) down to SAP (Finance).
Partner with engineering leadership to design and build an AI-First SDLC platform, embedding intelligent agents, LLMs, and automated reasoning into enterprise code generation, test creation, and deployment pipelines.
Architect next-generation, self-healing automation frameworks that utilize AI to dynamically adapt to UI/API changes across SaaS apps, ERPs, and custom microservices, drastically reducing test maintenance.
Act as the subject matter expert and champion for AI-native coding workflows. Drive the organization-wide adoption and best practices of advanced AI pair-programmers and terminals (e.g., Cursor, Claude Code, GitHub Copilot).
Evaluate, build, and integrate internal tooling, infrastructure-as-code, and advanced CI/CD continuous testing capabilities to optimize enterprise release velocity.
Participate in early-stage product and integration design reviews to ensure enterprise applications are designed for cross-functional testability, data integrity, and security.
Upskill and mentor Senior and Junior QE engineers in AI fluency, teaching teams how to write effective prompts, leverage AI agents for debugging, and securely integrate AI into their daily coding workflows.
Set the technical standard for automation code quality. Establish best practices, design patterns, and conduct rigorous code reviews for both human-written and AI-generated automation repositories.