Job Summary
The VP Engineering, Business AI Applications, will be the principal technical leader responsible for integrating Artificial Intelligence (AI) and Machine Learning (ML) capabilities across CPQ and NPI ecosystems, with a core focus on the Opportunity-to-Quote lifecycle. This executive role demands a blend of visionary leadership, deep technical architecture expertise, and hands-on experience building and deploying enterprise-grade AI products. The successful candidate will not only set the technical direction for transforming business applications with an AI mindset, but will also be accountable for the execution and successful delivery of scalable, reliable, and high-impact AI solutions that drive significant business growth and efficiency.
About the Team
The Business Applications & Engineering organization is a critical driver of Palo Alto Networks' operational and financial engine, engineering and scaling the enterprise business application portfolio that powers core corporate, customer, and revenue operations. As the business grows, the team is executing a high-impact application transformation, embedding state-of-the-art AI microservices, autonomous agentic workflows, and Large Language Models (LLMs) directly into business software systems to fundamentally redefine how enterprise teams operate across the company. This role sits at the intersection of business strategy and technical innovation, reporting directly to the SVP of Business Applications.
Your Impact
•Technology Strategy & Roadmap: Define the multi-year technology strategy and architecture roadmap for AI integration into the Business App stack, focusing on maximizing business value in areas like intelligent automation, predictive forecasting, and generative AI-assisted selling.
•AI Solution Architecture: Architect and establish company standards for advanced AI customer solutions, demonstrating viability through proof-of-concepts.
•Go-To-Market Engineering: Lead Go-To-Market Engineering to scale pipeline generation and seller productivity through AI, automation, and data-driven execution.
•Team Leadership: Lead and develop a global, multi-disciplinary team comprising AI solutions, full-stack, and SaaS developers, cultivating a collaborative, inclusive, and innovative culture.
•Strategic Impact: Drive significant business impact by leading large-scale, multi-organizational, or global initiatives.
•AI Governance and Ops: Drive an AI Ops culture to enable continuous evolution across all Business applications.
•Performance and Optimization: Monitor, analyze, and optimize the performance, cost-efficiency, and operational health of all deployed AI models and solutions, driving continuous improvement through data-driven insights.
Qualifications — Your Experience
Leadership and Communication
•Exceptional people-management skills, acting as a Player/Coach to inspire and foster desired behaviors.
•Outstanding verbal and written communication skills, coupled with executive presence.
•Strong enterprise communication skills, business acumen, and financial understanding.
•Proven track record of thought leadership (e.g., technical articles, conference speaking engagements, or open-source contributions).
Business and Strategic Acumen
•Extensive experience with Business applications, particularly the Opportunity to Quote lifecycle.
•Excellent analytical skills and a demonstrated ability to translate detailed data analysis into actionable strategic insights to drive customer adoption and provide business recommendations.
•Demonstrated ability to work effectively across internal and external organizations, building consensus and driving results.
•Proven ability to reduce organizational friction and implement scalable mechanisms.
Technical Leadership
Agentic AI & Generative Workflows
•Autonomous Sales & Commerce Agents: Proven experience building agentic workflows using frameworks like LangChain, LlamaIndex, AutoGPT, or CrewAI to automate multi-step commercial processes (e.g., automated RFPs, dynamic quote generation, automated lead nurture).
•RAG (Retrieval-Augmented Generation): Deep expertise in architecting high-accuracy RAG pipelines using vector databases (Pinecone, Milvus, Weaviate, Qdrant) to ground LLMs in internal product catalogs, pricing rules, and sales collateral.
•LLM Engineering & Fine-Tuning: Fine-tuning open-source models (Llama 3, Mistral) vs. orchestrating proprietary foundation models (OpenAI GPT-4, Anthropic Claude, Google Gemini); prompt engineering, guardrailing (NeMo Guardrails), and context window optimization.
Predictive Analytics & Machine Learning
•CPQ ML Engines: Hands-on architecture experience with personalized recommendation algorithms (Collaborative Filtering, Neural Collaborative Filtering, Graph Neural Networks), dynamic pricing engines, search relevance engines, and automated visual search.
•NPI agents: Swarm os agents to help accelerate the NPI velocity looking at E2E Opty to cash lifecycle.
CPQ & Revenue Ecosystem Architecture
•Commercial Platform Integrations: Direct experience embedding AI microservices into enterprise CRM and Commerce suites, including Salesforce SAP Commerce Cloud / CX, Adobe Commerce / Magento, Commercetools, or Zuora.
•Headless & Microservices Architecture: Experience deploying AI capabilities via API-first, composable architecture patterns (REST, GraphQL, gRPC) directly into customer-facing storefronts and seller interfaces.
MLOps, LLMOps & Enterprise Infrastructure
•Production MLOps: Enterprise experience with ML lifecycle management platforms (MLflow, Kubeflow, Databricks, AWS SageMaker, Vertex AI), model drift detection, automated retraining loops, and A/B testing infrastructure.
Preferred Qualifications
•Master's Degree or PhD in Engineering or a related STEM field is preferred.
•20+ years of experience in Product Application Engineering.
•Expertise in enterprise AI productivity tools and platforms.
•Modernization Mindset: Proven experience in leveraging AI and automation to modernize governance. Demonstrated success in transitioning programs from manual, spreadsheet-based processes to automated, self-healing platforms.
Compensation Disclosure
The compensation offered for this position will depend on qualifications, experience, and work location. For candidates who receive an offer at the posted level, the starting base salary (for non-sales roles) or base salary + commission target (for sales/commissioned roles) is expected to be the annual range listed below. The offered compensation may also include restricted stock units and a bonus.
$306,000.00 - $420,000.00/yr
Role is eligible for Immigration Sponsorship: Yes.
