Distinguished)

Palo Alto Networks

22h•Onsite

About The Position

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

Requirements

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.
Strong systems thinking and the ability to work across data, model, application, and infrastructure layers.
Ability to operate with incomplete information, break down ambiguous problems, and move from concept to implementation with appropriate judgment.

Nice To Haves

Experience in enterprise-scale environments with meaningful security, privacy, or compliance constraints.
Experience with telemetry, or production monitoring for AI systems.
Evidence of applied AI building, such as GitHub contributions, technical writing, demos, open-source work, or community participation.
Demonstrated curiosity and hands-on engagement with emerging AI tooling and signal-based learning communities.

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

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