VP Engineering, Business AI Applications

Palo Alto Networks•Office - USA - CA - Headquarters, CA

2d•$306,000 - $420,000•Onsite

About The Position

The VP Engineering, Business AI Applications is the principal technical leader responsible for integrating AI and machine learning across CPQ and NPI ecosystems, with a core focus on the Opportunity-to-Quote lifecycle. This executive role combines visionary leadership, technical architecture expertise, and experience building and deploying enterprise-grade AI products. The successful candidate sets the technical direction for transforming business applications with an AI mindset and is accountable for delivering scalable, reliable AI solutions that drive business growth and efficiency. The role is part of the Business Applications & Engineering organization, which engineers and scales enterprise business applications supporting corporate, customer, and revenue operations. The organization is embedding AI microservices, autonomous agentic workflows, and LLMs into business software. The position reports directly to the SVP of Business Applications. Most teams work from the office full time, with flexibility when needed. The position defines the multi-year technology strategy and architecture roadmap for AI integration; architects advanced AI customer solutions and establishes standards; leads go-to-market engineering to improve pipeline generation and seller productivity; and leads a global, multidisciplinary team of AI solutions, full-stack, and SaaS developers. The VP drives large-scale organizational initiatives, establishes an AI Ops culture, and monitors and optimizes the performance, cost-efficiency, and operational health of deployed AI models and solutions.

Requirements

Exceptional people-management skills and ability to act as a Player/Coach, inspire others, and foster desired behaviors.
Outstanding verbal and written communication skills and executive presence.
Strong enterprise communication skills, business acumen, and financial understanding.
Extensive experience with business applications, particularly the Opportunity-to-Quote lifecycle.
Excellent analytical skills and ability to turn detailed data analysis into strategic insights, customer-adoption efforts, and business recommendations.
Demonstrated ability to work effectively across internal and external organizations, build consensus, and drive results.
Proven ability to reduce organizational friction and implement scalable mechanisms.
Proven experience building agentic workflows with frameworks such as LangChain, LlamaIndex, AutoGPT, or CrewAI to automate multi-step commercial processes.
Deep expertise architecting high-accuracy RAG pipelines using vector databases such as Pinecone, Milvus, Weaviate, or Qdrant.
Experience fine-tuning open-source models or orchestrating proprietary foundation models; prompt engineering, guardrailing, and context-window optimization.
Hands-on architecture experience with CPQ machine-learning engines, including personalized recommendation algorithms, dynamic pricing, search relevance, and automated visual search.
Experience with NPI agents to accelerate the NPI velocity across the end-to-end Opportunity-to-Cash lifecycle.
Direct experience embedding AI microservices into enterprise CRM and commerce suites, such as Salesforce, SAP Commerce Cloud/CX, Adobe Commerce/Magento, commercetools, or Zuora.
Experience deploying AI capabilities using API-first, composable architecture patterns such as REST, GraphQL, and gRPC into customer-facing storefronts and seller interfaces.
Enterprise experience with production MLOps platforms such as MLflow, Kubeflow, Databricks, AWS SageMaker, or Vertex AI, including model drift detection, automated retraining loops, and A/B testing infrastructure.
Proven track record of thought leadership, such as technical articles, conference speaking engagements, or open-source contributions.

Nice To Haves

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.
Proven experience using AI and automation to modernize governance, including transitioning manual, spreadsheet-based processes to automated, self-healing platforms.

Responsibilities

Define the multi-year technology strategy and architecture roadmap for integrating AI into the Business Applications stack, emphasizing intelligent automation, predictive forecasting, and generative AI-assisted selling.
Architect advanced AI customer solutions and establish company standards, demonstrating viability through proof-of-concepts.
Lead Go-To-Market Engineering to scale pipeline generation and seller productivity through AI, automation, and data-driven execution.
Lead and develop a global, multidisciplinary team of AI solutions, full-stack, and SaaS developers, fostering a collaborative, inclusive, and innovative culture.
Drive significant business impact through large-scale, multi-organizational, or global initiatives.
Drive an AI Ops culture that enables continuous evolution across Business applications.
Monitor, analyze, and optimize the performance, cost-efficiency, and operational health of deployed AI models and solutions, using data-driven insights to drive continuous improvement.
Build consensus and drive results across internal and external organizations, reduce organizational friction, and implement scalable mechanisms.