Sr Director IT Engineering, GTM AI Applications

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

10d•$264,300 - $362,725•Onsite

About The Position

The Sr. Director IT Engineering, GTM AI Applications, will be the principal technical leader responsible for integrating Artificial Intelligence (AI) and Machine Learning (ML) capabilities across our Go-to-Market (GTM) technology ecosystem, 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 in building and deploying enterprise-grade AI products. The successful candidate will not only set the technical direction for transforming GTM 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. The GTM Technology team is a critical enabler of the company's revenue engine, responsible for the platform and applications that support Sales, and CPQ. We are embarking on a major transformation, leveraging cutting-edge AI technologies, particularly Large Language Models (LLMs), to fundamentally redefine how our GTM teams operate, from lead generation and opportunity management to proposal generation and quoting. This role sits at the intersection of business strategy and technical innovation, reporting directly to the SVP of GTM Technology.

Requirements

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, business acumen, and financial understanding.
Proven track record of thought leadership (e.g., technical articles, conference speaking, open source contributions).
Extensive experience with Go-to-Market (GTM) 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.
Agentic AI & Generative Workflows: 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.
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.
Sales Intelligence ML: Predictive lead scoring, opportunity win/loss modeling, churn prediction, account propensity-to-buy (P2B) scoring, and automated deal health assessments.
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.
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.

Nice To Haves

Master's Degree or PhD in Engineering or a related STEM field is preferred.
15+ 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.

Responsibilities

Define the multi-year technology strategy and architecture roadmap for AI integration into the GTM stack, focusing on maximizing business value in areas like intelligent automation, predictive forecasting, and generative AI-assisted selling.
Architect and establish company standards for advanced AI customer solutions, 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, multi-disciplinary team comprising AI Solutions, full-stack, and SaaS developers, cultivating a collaborative, inclusive, and innovative culture.
Drive significant business impact by leading large-scale, multi-organizational, or global initiatives.
Drive AI ops culture to drive continuous evolution of all GTM applications.
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.