AI Engineer, Enterprise AI (Principal/Sr. Principal/Distinguished)

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

 We are looking for 10x AI Engineers to help design, build, and operationalize AI-enabled workflows driving an AI-first enterprise. This role sits at the intersection of application engineering, automation, data integration, and AI systems deployment. The person in this role will harness the power of AI using strong systems thinking, model APIs, data sources, and workflow layers in ways that improve speed, quality, and business impact.

We are looking for engineers with an AI-first mindset who know how to use AI effectively and manage AI-enabled workflows to build practical technology solutions. This role is designed for systems-thinkers and orchestrators who can move fluidly between hands-on building and critically editing AI-generated solutions. You must have demonstrated expertise in workflow automation and enterprise integration, and the ability to operate effectively in ambiguous environments with sound technical judgment 

Key Responsibilities

• Build and deploy AI-enabled internal workflows that connect enterprise systems, data sources, model APIs, and automation layers.

• Design and improve retrieval, prompting, tool-calling, and orchestration patterns for internal use cases such as knowledge assistants, workflow automation, and decision support.

• Leverage AI-assisted development to accelerate implementation, bringing a strong 'editor' mindset to ruthlessly audit, review, and secure AI-generated code against our standards for reliability, security, observability, and documentation

• Develop and maintain evaluation approaches for output quality, retrieval accuracy, latency, and failure modes, and use findings to improve system performance over time.

• Lead rigorous problem formulation by partnering with IT, security, data, HR, Finance, and business stakeholders. You will ensure we are applying AI to solve the right business problems before translating those needs into scalable technical solutions

• Contribute reusable components, playbooks, and patterns that reduce duplicate effort and improve speed across the IT engineering environment.

• Help identify where human review, controls, and escalation paths are required to support responsible deployment of AI-enabled systems.

• Apply sound engineering judgment to balance speed, usability, risk, and maintainability in production and near-production environments.

• Demonstrated ability to tie technical architecture and AI implementations directly to measurable business outcomes, recognizing that technology serves the business

Qualifications

Required Qualifications

• 3 to 8 years of Agentic AI relevant experience in software engineering, IT engineering, platform engineering, workflow automation, or related technical roles.

• Demonstrated experience building or deploying applied AI systems in production or near-production environments.

• Hands-on experience with one or more of the following: LLM APIs, retrieval-augmented generation, workflow orchestration, agent or tool calling, prompt design, evaluation frameworks, or AI observability.

• Experience integrating applications, data sources, or internal platforms through APIs, services, event-driven patterns, or workflow tools.