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# Principal Software Automation/Test Engineer

- **Company:** Palo Alto Networks
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $156,400.0 - $253,000.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, ARM Architecture, Automation of Tests, Microsoft Azure, BigQuery, Software as a Service, Cloud Computing, Cloud Computing Security, Code Review, Cyber Security, Computer Programming, Continuous Integration, Data as a Services, DevOps, Distributed Systems, Github, Identity and Access Management, Python (Programming Language), Log Analysis, Microsoft UI Automation, NoSQL, Queueing Systems, Reliability Engineering, OpenAI, Cloud Services, Selenium, Software Engineering, SQL Databases, Software Testing Automation Framework, System Testing, Systems Integration, TypeScript, Data Logging, Google Cloud, Retrieval-Augmented Generation, Large Language Models, Grafana, Multi-Agent Systems, Mttr, Multi-Cloud, Parallel Computation, Agentic-AI, Build Management, Gitlab-ci, Kubernetes, Information Technology, Playwright, Production Code, Integration Frameworks, Apache Kafka, Cloud Connectors, Google Gemini, Terraform, Data Pipelines, Docker, Jenkins, Golang, Microservices
- **Published:** October 30, 2026
- **Apply:** https://dejobs.org/x/x/B9EFA24D840F48D5B3547A902D077087/job/

## About the Role

10+ years of software engineering experience, with at least 6 years focused on test automation, quality engineering, or developer productivity for large-scale distributed systems or SaaS

 * Strong programming skills in at least one of Python, Go, or Java, and the ability to write production-quality code, not just scripts
 * Proven record of architecting and building test automation frameworks from scratch and scaling them across multiple teams
 * Deep hands-on experience with CI/CD systems and practices (GitLab CI, Jenkins, GitHub Actions, or similar), including pipeline design, test parallelization, and environment provisioning
 * Strong cloud knowledge across GCP, AWS, and Azure: compute, networking, IAM, storage, managed data services, and cloud-native security concepts
 * Solid experience with containers and orchestration (Docker, Kubernetes/GKE/EKS/AKS) and Infrastructure as Code (Terraform, Helm)
 * Extensive experience with system, integration, end-to-end, performance, and scale testing of microservices and data pipelines (REST/gRPC APIs, message queues such as Pub/Sub or Kafka, SQL and NoSQL stores, BigQuery or similar)
 * End-to-end domain expertise across enterprise cybersecurity systems to lead our overall quality and system validation strategy.
 * Excellent troubleshooting skills across the full stack: application, infrastructure, network, and cloud provider layers, using observability tools such as Cloud Logging, Grafana, and tracing
 * A demonstrated ability to learn new, complex domains quickly and to deliver in ambiguous, fast-moving environments
 * Strong communication skills and the ability to influence technical decisions across organizations without direct authority
 * BS/MS in Computer Science, Engineering, or equivalent practical experience Big Plus - Hands-on experience building AI agents or LLM-powered applications (for example LangChain, LangGraph, MCP, OpenAI/Anthropic/Gemini APIs, RAG, tool calling, multi-agent orchestration)
 * Experience applying AI/ML to engineering workflows such as automated test generation, intelligent failure triage, or log and anomaly analysis
 * Experience testing or evaluating AI/LLM-based product features (evaluation frameworks, guardrails, non-deterministic output validation)
 * Experience with UI automation frameworks (Playwright, Selenium)
 * Experience with security, resilience, or chaos engineering practices

## Description

* Own the test architecture. Define and drive the automation strategy for Cortex Cloud services
 * Build world-class frameworks. Design, implement, and maintain scalable, maintainable test frameworks and tooling in Python, Go, Java, or TypeScript that engineering teams across the organization adopt.
 * Conquer complex integration environments. Quickly learn unfamiliar systems, then stand up and automate very complex integration environments: multi-cloud accounts, Kubernetes clusters, cloud connectors, data pipelines, scanners, brokers, and third-party integrations.
 * Make quality part of CI/CD. Build quality gates, test orchestration, ephemeral test environments, and release validation into CI/CD pipelines (GitLab CI, Jenkins, Flux, Helm, Terraform) so feedback is fast and reliable.
 * Test at cloud scale. Lead system, scale, and chaos testing for high-throughput distributed services, and find bottlenecks, race conditions, and failure modes before customers do.
 * Troubleshoot hard problems. Act as the escalation point for complex test and production-like failures across services, infrastructure, and cloud providers. Drive root-cause analysis using logs, metrics, traces, and data-store queries.
 * Bring AI into testing. Design and build AI agents and LLM-powered tools for test generation, failure triage, flaky-test detection, log analysis, and environment self-healing. Also define how AI-driven product features get validated.
 * Set the bar and mentor. Raise engineering standards through design reviews, code reviews, and best practices. Mentor senior and staff engineers, and influence technical direction across development, QA, and DevOps teams.
 * Measure and improve. Define quality metrics such as coverage, escape rate, pipeline health, flakiness, and MTTR, and use the data to improve release quality and speed.