AI-First Core IT Software Engineering: Software, ML & Data (Staff – Principal)

Our Mission

At Palo Alto Networks®, we’re united by a shared mission—to protect our digital way of life. We thrive at the intersection of innovation and impact, solving real-world problems with cutting-edge technology and bold thinking. Here, everyone has a voice, and every idea counts. If you’re ready to do the most meaningful work of your career alongside people who are just as passionate as you are, you’re in the right place.

Who We Are

In order to be the cybersecurity partner of choice, we must trailblaze the path and shape the future of our industry. This is something our employees work at each day and is defined by our values: Disruption, Collaboration, Execution, Integrity, and Inclusion. We weave AI into the fabric of everything we do and use it to augment the impact every individual can have. If you are passionate about solving real-world problems and ideating beside the best and the brightest, we invite you to join us!

We believe collaboration thrives in person. That’s why most of our teams work from the office full time, with flexibility when it’s needed. This model supports real-time problem-solving, stronger relationships, and the kind of precision that drives great outcomes.

Job Summary

Job Summary

We are hiring AI Engineers at multiple levels (Lead/Staff through Senior Principal) to join our IT Business Applications team. This group builds and operates the technology platforms that power Marketing, Sales, and the full Lead-to-Cash lifecycle.

This is a solutions engineering role, not a data science or ML research role. You will act as a hands-on technical leader who uses AI as building blocks—LLMs, agents, RAG, embeddings—to deliver production solutions that solve real business problems. You will partner directly with business and product stakeholders, translate requirements into scalable architecture, and own delivery end-to-end from design through production operations.

You will move beyond traditional software engineering to design intelligent, agentic workflows that revolutionize our Go-To-Market (GTM) and customer experience processes—ensuring our platforms are the most secure and efficient in the industry, directly improving operational revenue generation.

Your Impact

AI Architecture & Solution Design

• Business-to-Architecture Translation: Partner with business and product teams to deeply understand requirements. Translate business intent into scalable, modern technical architectures that meet non-functional requirements (performance, reliability, scalability, security, observability).

• AI Gateway Architecture: Architect a Proxy-First AI Ecosystem—an intermediary layer that ensures vendor independence, allowing seamless switching between LLMs (e.g., GPT-4, Llama, Anthropic) without refactoring core application code.

• Unified API Design: Build a single, unified API endpoint that abstracts the complexities of individual LLM providers, providing centralized control for access and security.

• Technology Selection: Evaluate and choose the right architecture patterns for each use case—selecting from agentic, RAG, workflow-based, hybrid, or traditional engineering approaches based on problem characteristics.

• Build vs. Buy Decisions: Make principled technology choices. Design for maintainability, extensibility, and operational excellence.

GenAI & Multi-Agent Systems

• Agentic AI Development: Design and build sophisticated Multi-Agent Systems and Agent-to-Agent (A2A) workflows capable of planning, multi-step execution, self-correction, and collaboration with humans or other agents.

• Business Workflow Automation: Apply GenAI to complex GTM business logic—automating workflows such as customer support, sales compensation, entitlement platforms, and partner channel operations.

• Lead-to-Cash Transformation: Own end-to-end delivery of MarTech and FinTech initiatives, from technical design to operational readiness, leveraging AI to transform quote-to-cash processes.

• Advanced Reasoning Pipelines: Develop retrieval systems, knowledge graph integrations, and reasoning pipelines that support autonomous task execution.

AI Observability & Quality

• LLM Observability: Implement comprehensive observability pipelines for GenAI—tracking trace-level data, prompt inputs/outputs, model latency, and cost.

• Cost & Performance Optimization: Design architecture that routes queries to the optimal