> Markdown version of [/jobs/ext/3808675-principal-solutions-architect](https://www.wearedevelopers.com/jobs/ext/3808675-principal-solutions-architect). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Solutions Architect - **Company:** Palo Alto Networks - **Location:** Santa Clara, CA, United States - **Experience:** Expert - **Salary:** $175,400.0 - $283,800.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Mobile Application Development, Cloud Computing, System Configuration, Data Architecture, Information Engineering, Dataspaces, Machine Learning, Natural Language Processing, Scrum Methodology, Software Deployment, Software Engineering, Systems Integration, Google Cloud, System Availability, Large Language Models, IT Architecture, Deep Learning, Generative AI, Agentic-AI, Event Driven Architecture, Data Lakes, Information Technology, Enterprise Integration, Machine Learning Operations, Data Pipelines, Legacy Systems - **Published:** October 17, 2026 - **Apply:** https://www.themuse.com/jobs/paloaltonetworks/principal-solutions-architect-9d5a79 ## About the Role * Minimum Bachelor's Degree in Computer Science, Data Science, Engineering or a related field with 15+ years of experience or a Ph.D in related filed with 10+ years of experience in enterprise architecture, software engineering, or data architecture * AI Domain Expertise: Profound understanding of AI/ML concepts, generative AI paradigms, natural language processing, and deep learning, paired with a proven understanding of how AI can solve complex business problems and drive ROI. * Systems Thinking & Integration: Proven track record of designing complex AI integrations (APIs, event-driven architectures, data pipelines, model orchestration) across large-scale enterprise ecosystems and cloud platforms. * First-Principles Mindset: Ability to strip away assumptions, drill down to the fundamental truths of a business or technical problem, and build innovative solutions upward. * Executive Presence: Exceptional communication skills. You are comfortable translating deeply technical machine learning concepts into business value for C-suite executives and driving consensus among diverse stakeholders. * Self-Starter Mentality: High level of autonomy and initiative. You thrive in ambiguity, take ownership of challenges, and independently drive initiatives to completion. * Team-Oriented: A strong collaborator who believes that the best architectures are built through shared knowledge, low ego, and teamwork. * Hands-on AI Application: Experience using AI-driven tools, model orchestration frameworks, building and deploying scalable machine learning models into production., * Advanced AI & Data Architecture: Hands-on experience designing architectures that utilize Large Language Models (LLMs), Agentic frameworks, Retrieval-Augmented Generation (RAG), and Vector Databases within an enterprise context. * Platform Certifications: Recognized architectural certifications in leading enterprise cloud platforms (AWS, GCP, Azure) with a specific focus on Data & Machine Learning specialties. * Complex Industry Experience: Proven experience navigating the unique complexities of deploying AI in high-volume, highly regulated, or complex data ecosystems. * Custom Application Building: Experience with building custom AI-native applications or integrating foundational models into existing legacy platforms. * Agile Proficiency: Experience with Agile development methodologies (Scrum/Kanban) applied specifically to data science and AI lifecycles. * Leadership Track Record: Prior experience working in a Principal or Director-level capacity, demonstrating ownership over a significant product area or enterprise AI capability. ## Description We are seeking a visionary, future-focused Principal AI Solution Architect to lead the solution strategy, architectural design, and enterprise-wide AI transformation. In this highly visible role, you will serve as the strategic bridge between core business needs and emerging artificial intelligence capabilities. You will not just be designing traditional system configurations; you will be applying a first-principles mindset to embed generative AI, intelligent automation, predictive analytics, and machine learning into our core business workflows and products. By understanding the deep domain nuances of AI models, data engineering, and MLOps (Machine Learning Operations), you will connect the dots across disparate systems and data silos. You will partner with cross-functional teams to build a unified, scalable, secure, and AI-optimized technical solution architecture., * AI-Infused Architectural Leadership: Drive the end-to-end technical architecture for our enterprise AI initiatives, proactively embedding advanced AI and machine learning capabilities (e.g., LLMs, agentic workflows, predictive modeling, and intelligent automation) into business operations and customer-facing products. * Cross-Functional Alignment: Act as a strategic partner to the business. Bring leaders from Product, Engineering, Data, Security, and IT along on the journey, bridging the gap between business strategy and technical execution to create a unified AI architecture. * Enterprise Integration: Design holistic integration strategies. You will look beyond standalone models to understand how AI interacts with the entire enterprise ecosystem, including data lakes, core transactional systems (CRM, ERP), cloud infrastructure, and user-facing applications. * Champion Best Practices: Establish and enforce best-in-class industry design principles. Ensure all solutions prioritize Responsible AI/ethics, data privacy, security, high availability, performance, and the scalability of ML pipelines. * Connect the Dots: Proactively identify dependencies, risks, and synergies across different technical domains, data sources, and ongoing projects. Ensure isolated systems and fragmented data are woven into a cohesive, streamlined AI architecture. * Collaborative Mentorship: Act as a self-starter and a core team collaborator. Guide, mentor, and elevate the technical capabilities of junior architects, data scientists, and machine learning engineers. * Player-Coach Mindset: Drive technical and functional architecture with a hands-on approach to ensure AI architectural excellence from concept through to production deployment.