Global Solutions Architect - AI and Automation

Palo Alto Networks • Remote - USA - CA, TX

2d • $175,200 - $240,900 • Remote

About The Position

The Global Solutions Architect for AI and Automation owns the automation strategy for network security delivery within Palo Alto Networks’ Technical Services Center of Excellence. The role covers traditional automation—including scripts, API integrations, configuration management, migrations, validation, and workflow orchestration—and AI-enabled capabilities for assessing deployments, prescribing improvements, automating migrations, and supporting consultants worldwide.

This is a full-stack engineering role spanning frontend and backend development, data, cloud infrastructure, CI/CD, networking, and cybersecurity. The architect designs and operates production platforms and multi-agent AI systems, applies zero trust principles, secures AI systems against threats such as prompt injection and excessive agency, and delivers production-ready tooling and capabilities to Customer Success and Professional Services teams.

The role connects business leadership, thought leadership, and technical execution. It translates Technical Services priorities into architecture, roadmaps, and delivered capabilities; provides regular updates to senior leadership; collaborates across business units; mentors engineers; and shapes the organization’s approach to secure AI and network security. The position is remote and works with global teams across geographies.

Requirements

• 8–12 years of professional software development experience, including designing, shipping, and continuing to own at least one full-stack production system.
• Expert full-stack engineering across modern frontend frameworks, backend APIs and services, relational data modeling and SQL, cloud infrastructure, containers and Kubernetes, infrastructure as code, and CI/CD.
• Working proficiency in TypeScript and Python.
• Ability to demonstrate first-principles architecture decisions, including identifying constraints, choosing what not to build, and justifying the outcome.
• Software design and testing discipline, including clear interfaces, separation of concerns, dependency injection, and automated tests enforced in CI.
• Strong cybersecurity foundation covering OWASP Top 10, secure coding, threat modeling, OAuth 2.0, OIDC, mTLS, RBAC, secrets and key management, and secure software supply-chain practices.
• Ability to explain and apply NIST SP 800-207 and the CISA Zero Trust Maturity Model to a system built or secured.
• Network and security engineering experience with routing, segmentation, policy, VPN, SASE, and threat prevention, including deployment and testing against a firewall, SASE, or SD-WAN environment.
• Hands-on experience with LLMs, agents, skills, MCP, and coding agents; advanced daily use of Claude Code or a similar agent harness, including multi-agent workflows, custom skills, hooks, sub-agents, and MCP integrations.
• Deep understanding of harness engineering, including context and memory management, tool and skill design, guardrails and permissions, verification loops, evaluation, and cost and token control.
• Traditional automation experience with REST and XML APIs, Python or Bash scripting, configuration-as-code, infrastructure as code, and workflow orchestration; ability to explain when deterministic automation is preferable to an AI agent.
• Advanced understanding of LLM behavior, context windows, tool calling, structured outputs, retrieval-augmented generation, embeddings, evaluation, hallucination, and non-determinism.
• Production experience building and operating at least one LLM-backed application with tool-using agents, including designing tools, implementing the agent loop, and evaluating output quality.
• Working knowledge of the OWASP Top 10 for LLM Applications and Agentic Applications, MITRE ATLAS, and the NIST AI Risk Management Framework, with the ability to describe implemented controls for AI-system risks.
• Production experience on a major cloud and with infrastructure as code; Google Cloud is preferred, including GKE, Cloud SQL, IAM, and Secret Manager or KMS.
• Ability to present regularly to senior leadership, frame technical work in business terms, and produce design documents and status reports covering risks, critical path, and delivery forecast.
• Track record of building relationships and driving outcomes across teams and business units not directly managed, including Customer Success and Professional Services field teams.
• Comfort working asynchronously with global teams across time zones.
• Bachelor’s degree in Computer Science, Software Engineering, Electrical or Computer Engineering, or a related technical field.

Nice To Haves

• Hands-on experience with PAN-OS, Panorama, Strata Cloud Manager, or Prisma Access, or a certification such as PCNSE.
• Experience integrating AI security platforms such as Prisma AI Runtime Security (AIRS), or AI guardrail and red-teaming tooling.
• Familiarity with Google SAIF, ISO/IEC 42001, or the NIST AI 600-1 Generative AI Profile.
• Experience with AI red teaming, LLM evaluation pipelines, or AI observability and tracing.
• Experience with network automation tools such as Ansible, Terraform providers for network devices, or pan-os-python and similar SDKs.
• Experience with Google ADK, Claude Agent SDK, or authoring and publishing MCP servers, skills, or plugins for others to use.
• Experience producing technical documentation, enablement programs, or service offerings (SKUs) in a professional services or customer success organization.
• Experience with Bun, Hono, Zod, Drizzle, or Vite.
• Experience with Cortex or Cloud-Delivered Security Services.
• Experience with multi-region architecture, data residency, or cloud FinOps.
• Security certifications such as CISSP, CCSP, or OSCP.
• Open-source maintainership or public SDK contributions.
• Experience leading a small engineering team or practice, including onboarding and enablement.

Responsibilities

• Own the Technical Services automation portfolio, including scripts, API integrations, configuration migration and validation tooling, and workflow orchestration; select deterministic automation or AI according to the problem.
• Architect and own production platforms that run maturity and security assessments across NGFW, Strata Cloud Manager, Prisma Access, Cloud-Delivered Security Services, and Prisma SD-WAN.
• Design assessment catalogs, parsers, and evaluators with measurable accuracy, including parser coverage, license-entitlement gating, and measured readiness.
• Design and operate multi-agent AI systems on frontier models, including tool design, agent loops, structured outputs, retrieval, refusal handling, and output-quality evaluation.
• Apply zero trust principles, including per-request identity-based access, least-privilege and short-lived credentials, workload segmentation, encryption, continuous verification, and telemetry.
• Threat-model and secure AI systems, defend against prompt injection, constrain agent autonomy and tool permissions, protect sensitive data, and add guardrails and human approval where actions carry risk.
• Build full-stack features in TypeScript, Python, and React, incorporating authorization, tenant isolation, credential encryption, and audit logging.
• Operate CI/CD pipelines and Terraform-managed infrastructure on GKE across multiple regions, owning supply-chain controls, data residency, rollout safety, observability, and incident follow-through.
• Validate platform changes against live firewall, Prisma Access, and SD-WAN lab environments before release.
• Lead harness engineering for the Center of Excellence, including agent harnesses, skills, MCP servers, sub-agent roles, hooks, context files, and quality gates for safe and productive coding-agent use.
• Improve Center of Excellence productivity across its release schedule by applying automation and AI to documentation, enablement content, service SKU development, and launch readiness.
• Translate Technical Services priorities into architecture, roadmaps, and delivered capabilities, and explain technical trade-offs in business terms.
• Provide regular senior-leadership updates on progress, risks, automation ROI, adoption, engagement outcomes, and AI spend.
• Deliver tooling, enablement, and reference architectures to Customer Success and Professional Services teams, and use their feedback to inform future work.
• Publish guidance, present at internal forums, and shape the practice’s point of view on secure AI adoption and network security.
• Collaborate across Product, Engineering, Sales, Security, IT, and other business units to remove friction and improve execution.
• Contribute to open-source SDKs for Palo Alto Networks APIs.
• Mentor engineers, publish reference implementations, and hold regular office hours.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior