Senior Engineering Manager, Enterprise AI Products

About the role

The Senior Engineering Manager for the Enterprise AI Products Team leads the end-to-end execution behind Palo Alto Networks' next generation of internal and external AI-driven applications, intelligent agents, and automated solutions. This role requires a sharp, deeply technical manager who excels in both technical depth and horizontal breadth.

Responsibilities

• Maintain strong technical anchoring by engaging in system design, performing rigorous code reviews, and diving into critical production issues.
• Solve complex enterprise challenges by balancing technical depth with a broad understanding of business functions like Marketing, Finance, and Sales.
• Lead a talent-dense team through the full lifecycle of production-grade AI applications, multi-modal systems, and agent workflows.
• Translate complex, fast-moving business requirements into concrete technical roadmaps and hardened software foundations.
• Focus ruthlessly on developer velocity and systems thinking by designing internal tooling, automated evaluation pipelines, and guardrails.
• Instill high-rigor engineering practices, including automated model training, versioning, deployment, and monitoring.
• Drive automated testing strategies for drift detection and build self-healing infrastructure to minimize operational toil.
• Partner with security, legal, and data governance teams to build privacy-preserving data interfaces and ensure compliance with AI deployment practices.

Qualifications

• 10+ years of experience in software engineering, distributed systems, or enterprise architecture, with at least 4+ years of direct engineering management experience.
• Active Technical Roots: Proven track record as an individual contributor with fluency in programming languages such as Python, Go, or Java.
• Production Generative AI & LLM Systems: Direct experience building, deploying, and scaling enterprise applications utilizing Large Language Models (LLMs), RAG, and multi-modal agent frameworks.
• Infrastructure & MLOps Fluency: Deep familiarity with modern AI/ML frameworks (PyTorch, TensorFlow) and cloud ecosystems (AWS SageMaker, Google Vertex AI, Azure ML).
• Cross-Functional Collaboration: Proven ability to partner with and influence non-engineering functions like Product, Finance, Legal, and HR.
• Thriving in Ambiguity: Experience navigating highly fluid, rapid-growth technical environments.
• Elite Communication: Exceptional ability to distill complex architectural bottlenecks into clear, actionable narratives.
• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical discipline.
• Prior experience in a technical lead, technical consulting, or solutions architecture capacity, acting as a bridge between deep engineering groups and diverse business domains.
• Deep understanding of cybersecurity principles specifically applied to guarding, monitoring, and deploying secure AI systems.
• Experience contributing to open-source AI projects or a history of bringing cutting-edge research workflows into enterprise-grade products.