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Palo Alto Networks

Builds cybersecurity tools for organizations

Web Development Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeSanta Clara, CA, USA

About the Job

Requirements

  • A bachelor's or master's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related technical discipline.
  • Approximately 10 or more years of experience in software engineering, application development, AI engineering, or related technical roles, with demonstrable hands-on AI application development experience.
  • Strong proficiency in Python and experience building production-grade applications, services, and integrations.
  • Experience working with large language models, generative AI APIs, prompt engineering, embeddings, vector search, and retrieval-augmented generation architectures.
  • Strong understanding of REST APIs, distributed systems, application architecture, data processing, and software engineering best practices.
  • Experience with JavaScript or TypeScript and modern web technologies such as React, Next.js, Node.js, or comparable frameworks.
  • Familiarity with web development workflows, content management system platforms, content publishing operations, and digital experience ecosystems.
  • Experience with Git, automated testing, continuous integration and continuous delivery pipelines, containerization, and cloud-based application deployment.
  • Understanding of application security, data privacy, responsible AI, and production software quality standards.
  • Experience with enterprise content management systems and digital experience platforms such as Adobe Experience Manager, Sitecore, Contentful, or comparable solutions.
  • Experience with marketing technology platforms, digital analytics, personalization, experimentation, and customer journey optimization.
  • Familiarity with agentic AI frameworks and orchestration tools such as LangGraph, LangChain, Semantic Kernel, or equivalent technologies.
  • Experience with vector databases and retrieval technologies such as Weaviate, FAISS, Elasticsearch, or cloud-native alternatives.
  • Experience with cloud AI services and infrastructure on AWS, Microsoft Azure, or Google Cloud.
  • Familiarity with large language model evaluation frameworks, guardrails, observability, model monitoring, and LLMOps practices.
  • Experience developing enterprise copilots, internal AI assistants, workflow automation tools, or multi-agent systems.
  • Familiarity with web standards, semantic HTML, CSS, JavaScript performance optimization, Core Web Vitals, and Web Content Accessibility Guidelines.
  • Technical skills include generative AI, large language models, prompt engineering, embeddings, retrieval-augmented generation, AI agents, model evaluation, guardrails, Python, JavaScript/TypeScript, React or equivalent frameworks, Node.js, REST APIs, HTML5, CSS3, vector databases, document processing, API integration, structured data, retrieval pipelines, enterprise knowledge systems, AWS, Azure or Google Cloud, Docker, Git, continuous integration and continuous delivery, automated testing, monitoring, deployment automation, content management systems, web production workflows, personalization, search engine optimization, digital analytics, experimentation, content lifecycle management, AI evaluation, accessibility validation, secure coding, data privacy, content governance, and production observability.

Responsibilities

  • Design and implement AI-powered tools that accelerate front-end and back-end web development, component creation, code generation, refactoring, and documentation.
  • Integrate large language model-based coding assistants and agentic workflows into existing development environments and software development lifecycle processes.
  • Develop reusable AI capabilities for generating web components, page templates, design system implementations, and code aligned with enterprise standards.
  • Build AI-driven solutions to assist with code reviews, debugging, test generation, accessibility compliance, and performance optimization.
  • Collaborate with engineering teams to integrate AI into modern web architectures, APIs, content management systems, and marketing technology platforms.
  • Establish guardrails to ensure AI-generated code meets standards for security, maintainability, scalability, and brand consistency.
  • Build AI-driven automation solutions to streamline end-to-end web production, including page creation, content ingestion, metadata generation, asset management, localization, and publishing workflows.
  • Develop AI assistants that support web producers in creating, updating, validating, and maintaining website pages at scale.
  • Integrate AI capabilities with enterprise content management systems and digital experience platforms to reduce repetitive manual tasks and accelerate time to publish.
  • Implement automated validation for broken links, missing metadata, content inconsistencies, image alt text, page structure, and publishing readiness.
  • Use generative AI to assist with content transformation, content tagging, taxonomy mapping, structured content generation, and content reuse across digital channels.
  • Introduce human-in-the-loop review and approval workflows for AI-generated content and production changes.
  • Design, develop, and deploy AI applications using commercial and open-source large language models.
  • Build retrieval-augmented generation solutions that retrieve relevant information from approved enterprise knowledge bases, content repositories, brand guidelines, web documentation, and technical standards.
  • Develop AI agents and multi-step workflows to automate complex web development and digital production tasks.
  • Apply prompt engineering, context management, structured outputs, tool calling, and model evaluation techniques to improve the reliability of AI applications.
  • Integrate AI services through REST APIs, software development kits, and enterprise integration frameworks.
  • Evaluate models and frameworks based on quality, latency, cost, security, and suitability for enterprise digital marketing use cases.
  • Develop AI-powered capabilities for website personalization, content recommendations, audience segmentation, and contextual digital experiences.
  • Leverage website analytics, customer journey data, and campaign performance insights to identify opportunities for AI-driven optimization.
  • Support experimentation and conversion rate optimization through AI-assisted hypothesis generation, content variant creation, and analysis of A/B testing results.
  • Build intelligent solutions for search engine optimization and content discoverability, including metadata recommendations, structured data validation, and content quality analysis.
  • Collaborate with analytics and marketing technology teams to integrate AI insights into existing reporting, experimentation, and optimization workflows.
  • Apply responsible data practices and consent requirements when working with customer or behavioral data.
  • Design scalable, maintainable, and observable AI applications and services suitable for enterprise deployment.
  • Implement continuous integration and continuous delivery integrations, automated testing, version control, deployment pipelines, and release management practices for AI-powered solutions.
  • Apply MLOps and LLMOps principles, including model evaluation, prompt versioning, monitoring, tracing, and lifecycle management.
  • Optimize AI workloads for response time, throughput, reliability, and cost efficiency.
  • Document architectures, APIs, technical decisions, operational procedures, and reusable implementation patterns.
  • Partner with platform engineering, cybersecurity, legal, privacy, and IT teams to ensure production readiness and responsible AI adoption.
  • Work with Digital Experience Marketing leadership to identify high-value AI use cases across web development and web production operations.
  • Translate business requirements into technical solutions, prototypes, proof-of-concepts, and production implementations.
  • Partner with UX, content, search engine optimization, analytics, marketing operations, and web platform teams to ensure solutions address operational needs.
  • Establish reusable AI capabilities, engineering standards, reference architectures, and best practices across digital experience teams.
  • Document architecture, reusable patterns, implementation guidelines, and best practices for AI adoption.
  • Stay current with emerging AI engineering techniques, agentic workflows, web automation tools, and digital experience technologies.
  • Champion responsible AI practices, including human oversight, data privacy, security, explainability, and governance.

Desired Qualifications

  • Experience with enterprise content management systems and digital experience platforms such as Adobe Experience Manager, Sitecore, Contentful, or comparable solutions.
  • Experience with marketing technology platforms, digital analytics, personalization, experimentation, and customer journey optimization.
  • Familiarity with agentic AI frameworks and orchestration tools such as LangGraph, LangChain, Semantic Kernel, or equivalent technologies.
  • Experience with vector databases and retrieval technologies such as Weaviate, FAISS, Elasticsearch, or cloud-native alternatives.
  • Experience with cloud AI services and infrastructure on AWS, Microsoft Azure, or Google Cloud.
  • Familiarity with large language model evaluation frameworks, guardrails, observability, model monitoring, and LLMOps practices.
  • Experience developing enterprise copilots, internal AI assistants, workflow automation tools, or multi-agent systems.
  • Familiarity with web standards, semantic HTML, CSS, JavaScript performance optimization, Core Web Vitals, and Web Content Accessibility Guidelines.

About the Company

Palo Alto Networks

Builds cybersecurity tools for organizations

Palo Alto Networks builds cybersecurity hardware and software for businesses and public institutions, protecting their networks, cloud workloads and digital identities. Its portfolio combines network firewalls with tools that detect attacks, investigate security alerts and manage access to sensitive systems. Security teams use these products across offices, data centers and cloud environments, with shared data and automated workflows connecting the company's platforms. It earns revenue from appliance sales and software licenses, plus subscriptions and support contracts that keep protections and services active over time. Palo Alto Networks began operations in 2005 and is headquartered in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
2005

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