Palo Alto Networks
VP Engineering, Business AI Applications
$306K–$420K
Office - USA - CA - Headquarters · No · Director · Full-time · Posted Oct 29, 2026
- Experience
- 20+ years of Product Application Engineering is listed as a preferred qualification; no required years are stated.
- Education
- Master's Degree or PhD in Engineering or a related STEM field is preferred.
- Visa sponsorship
- Eligible for immigration sponsorship: Yes.
About the job
Leads the strategy, architecture, and delivery of AI and machine-learning capabilities across business applications, especially the Opportunity-to-Quote lifecycle, to improve automation, forecasting, seller productivity, and business performance.
What you'll do
- Set the multi-year AI technology strategy and architecture roadmap for business applications, and establish standards for AI solutions.
- Lead global multidisciplinary teams and Go-To-Market Engineering initiatives that scale AI-driven pipeline generation and seller productivity.
- Oversee AI governance and operations, and optimize deployed models for performance, cost, and reliability.
What they ask for
- Extensive experience with business applications, particularly the Opportunity-to-Quote lifecycle.
- Proven experience building agentic workflows to automate multi-step commercial processes.
- Deep expertise architecting RAG pipelines using vector databases to ground LLMs in internal business content.
- Hands-on architecture experience with CPQ machine-learning capabilities such as recommendation, dynamic pricing, search relevance, or visual search.
Nice to have
- Master’s degree or PhD in Engineering or a related STEM field.
- 20+ years of experience in Product Application Engineering; expertise in enterprise AI productivity tools and platforms is also preferred.
Pay
$306,000.00 - $420,000.00/yr (USD, base salary)
Tools
LangChain, LlamaIndex, CrewAI, Pinecone, Weaviate, OpenAI GPT-4, Salesforce, MLflow
AI focus
Integrates and operates agentic AI, generative AI, RAG, predictive analytics, and ML in business applications, including CPQ and NPI; focuses on commercial automation, forecasting, seller productivity, and AI governance.
Model-written summary · the original posting is authoritative