Web Development Engineer

Palo Alto Networks•Office - USA - CA - Headquarters, CA

7d•$175,400 - $283,800•Onsite

About The Position

Palo Alto Networks is seeking an experienced Web Development Engineer to drive automation across its Digital Experience Marketing organization, focusing on web development, web production, and digital experience optimization. Based in Santa Clara, the role partners with web developers, digital marketers, UX/UI designers, content strategists, web producers, SEO specialists, and enterprise technology teams to design, develop, and deploy AI-powered solutions that improve development efficiency, accelerate content production, enhance digital experiences, and optimize marketing performance. The role combines software engineering with hands-on Generative AI, LLM, AI agent, RAG, and automation expertise. It translates digital marketing and web operations challenges into scalable, secure, production-ready AI solutions, helping move web operations toward a more intelligent, automated, and data-driven model. The engineer will develop AI-powered web development and production tools; integrate LLMs and agents into development and publishing workflows; build RAG applications, assistants, and automation; support personalization, SEO, analytics, and experimentation; and ensure solutions are scalable, observable, secure, and responsibly governed. The position also establishes reusable capabilities and practices while collaborating across digital experience, engineering, marketing, security, legal, privacy, and IT teams.

Requirements

Bachelor's or Master's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related technical discipline.
Approximately 10+ 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 with LLMs, Generative AI APIs, prompt engineering, embeddings, vector search, and RAG 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, CMS platforms, content publishing operations, and digital experience ecosystems.
Experience with Git, automated testing, CI/CD pipelines, containerization, and cloud-based application deployment.
Understanding of application security, data privacy, responsible AI, and production software quality standards.
Strong analytical, problem-solving, communication, and cross-functional collaboration skills.

Nice To Haves

Experience with enterprise CMS and digital experience platforms such as Adobe Experience Manager (AEM), 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 LLM 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 WCAG accessibility guidelines.

Responsibilities

Design and implement AI-powered tools for front-end and back-end development, component creation, code generation, refactoring, and documentation.
Integrate LLM coding assistants and agentic workflows into development environments and SDLC processes.
Develop reusable AI capabilities for web components, page templates, design system implementations, and enterprise-standard code.
Build AI-assisted solutions for code review, debugging, test generation, accessibility, and performance optimization.
Integrate AI with web architectures, APIs, CMS platforms, and marketing technology platforms.
Establish guardrails for security, maintainability, scalability, and brand consistency in AI-generated code.
Automate web production workflows, including page creation, content ingestion, metadata generation, asset management, localization, and publishing.
Develop assistants that help web producers create, update, validate, and maintain website pages at scale.
Implement automated validation for links, metadata, content consistency, alt text, page structure, and publishing readiness.
Use Generative AI for content transformation, tagging, taxonomy mapping, structured content generation, and content reuse.
Introduce human-in-the-loop review and approval workflows for AI-generated content and production changes.
Design and deploy AI applications using commercial and open-source LLMs, including RAG solutions using approved enterprise knowledge sources.
Develop AI agents and multi-step workflows for web development and digital production tasks.
Apply prompt engineering, context management, structured outputs, tool calling, and model evaluation to improve reliability.
Integrate AI services through REST APIs, SDKs, and enterprise integration frameworks; evaluate models and frameworks for quality, latency, cost, security, and suitability.
Develop AI capabilities for personalization, content recommendations, audience segmentation, and contextual digital experiences.
Use website analytics, customer journey data, and campaign performance insights to identify AI-driven optimization opportunities.
Support experimentation and conversion optimization through AI-assisted hypotheses, content variants, and A/B test analysis.
Build solutions for SEO and content discoverability, including metadata recommendations, structured data validation, and content quality analysis.
Integrate AI insights with reporting, experimentation, and optimization workflows, applying responsible data and consent practices.
Design scalable, maintainable, observable AI applications and services for enterprise deployment.
Implement CI/CD integrations, automated testing, version control, deployment pipelines, and release management.
Apply MLOps and LLMOps practices, 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 on production readiness and responsible AI adoption.
Identify high-value AI use cases with Digital Experience Marketing leadership and translate business needs into prototypes, proofs of concept, and production implementations.
Collaborate with UX, content, SEO, analytics, marketing operations, and web platform teams to address operational needs.
Establish reusable AI capabilities, engineering standards, reference architectures, and best practices across digital experience teams.
Stay current with AI engineering, agentic workflows, web automation, and digital experience technologies; champion human oversight, privacy, security, explainability, and governance.