Technical Enablement Engineer (AgentiX Support) Cortex

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 Are you an Technical Enablement Engineer? ready to translate raw model output into a reliable, high-quality production system? Do you excel at the craft of Prompt Engineering and thrive on the challenge of ensuring Generative AI output meets rigorous quality standards in mission-critical applications? Join our R&D Operations Team. You will be the Technical Enablement Engineer responsible for the end-to-end quality, performance, and operational tuning of our Generative AI-driven support system (“Co-Pilot”). Your mission is to actively shape the model's intelligence, govern the data it uses, and implement the mechanisms that guarantee its accuracy, directly accelerating our customer support engineering velocity. Your Impact Support & Customer Advocacy : Champion the Support Journey in Engineering. Develop in-product support instructions that reflect and address real incoming cases, enhancing the quality and effectiveness of AI feature solutions. Model Quality Validation : Use existing evaluation platforms and methodologies to validate production models. Monitor quality metrics to continuously assess and rank AI answers for accuracy and reliability. Prompt Engineering & CT Loop : Drive the Continuous Training loop through systematic prompt engineering (refining and versioning inputs). Analyze failures to define R&D actions or features needed to close model performance gaps. AI Knowledge Governance : Act as AI Content Governor, implementing controls to verify and ingest compliant knowledge base content, ensuring a quality data source. Cross-Functional SME : Serve as the AI Support Co-pilot Subject Matter Expert, partnering with R&D and Support Enablement to translate quality issues into core model logic improvements and feature development.