Sr Staff Engineer Software (AI/ML)

Palo Alto Networks•Santa Clara, CA

1d•Onsite

About The Position

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 Design and build agent-driven systems leveraging ADK to enable generative input/output orchestration across internal and customer-facing platforms. Define and implement the A2UI (Agent-to-User Interface) layer to translate agent reasoning and outputs into intuitive visual experiences and actionable workflows. Develop Model Context Protocol (MCP) integrations to enable secure and scalable context sharing between models, tools, and enterprise systems. Develop Agent-to-Agent (A2A) collaboration frameworks to enable multi-agent orchestration and task delegation aligned with the organization’s AI North Star. Build and maintain reusable agent capabilities, tools, and connectors to accelerate development of AI-powered workflows. Create visualization and interaction layers for generative outputs, enabling traceability, explainability, and human-in-the-loop feedback loops. Establish best practices for prompt design, agent memory management, tool usage, and context optimization within the agent ecosystem. Develop evaluation frameworks and telemetry to measure agent performance, response quality, and user engagement across AI-powered experiences. Implement governance mechanisms for agent safety, observability, and policy compliance in generative AI systems. Collaborate with product and design teams to define next-generation AI-native user experiences powered by autonomous agents and generative interfaces. Continuously evaluate emerging agent frameworks, LLM tooling, and generative AI technologies to inform platform architecture and roadmap decisions.

Requirements

2+ years of experience using an Object–Oriented programming language (Java/Python)
Experience with cloud-native service development stack on GCP
Knowledge of Object Oriented Programming design concepts
Solid grasp of RESTful API design and micro services architecture.
Skilled in diagnosing and solving complex problems while providing detailed technical analysis
Attention to details and high behavioral standards
Team player with can-do attitude to tackle difficult problems and you inspire your team to do the same
High energy and the ability to work in a fast-paced environment
Excellent collaboration and communication with multiple teams
Fast learner and eager to absorb new emerging technologies
M.S./B.S. degree in Computer Science or Electrical Engineering or equivalent military experience

Nice To Haves

Basic understanding of machine learning concepts and familiarity with ML frameworks (e.g., TensorFlow, PyTorch) is a plus.

Responsibilities

Design and build agent-driven systems leveraging ADK to enable generative input/output orchestration across internal and customer-facing platforms.
Define and implement the A2UI (Agent-to-User Interface) layer to translate agent reasoning and outputs into intuitive visual experiences and actionable workflows.
Develop Model Context Protocol (MCP) integrations to enable secure and scalable context sharing between models, tools, and enterprise systems.
Develop Agent-to-Agent (A2A) collaboration frameworks to enable multi-agent orchestration and task delegation aligned with the organization’s AI North Star.
Build and maintain reusable agent capabilities, tools, and connectors to accelerate development of AI-powered workflows.
Create visualization and interaction layers for generative outputs, enabling traceability, explainability, and human-in-the-loop feedback loops.
Establish best practices for prompt design, agent memory management, tool usage, and context optimization within the agent ecosystem.
Develop evaluation frameworks and telemetry to measure agent performance, response quality, and user engagement across AI-powered experiences.
Implement governance mechanisms for agent safety, observability, and policy compliance in generative AI systems.
Collaborate with product and design teams to define next-generation AI-native user experiences powered by autonomous agents and generative interfaces.
Continuously evaluate emerging agent frameworks, LLM tooling, and generative AI technologies to inform platform architecture and roadmap decisions.