Vp, Revenue Applied AI & Data

Nintex Nintex · Enterprise · Bellevue, WA · Revenue Operations

VP leading strategy, architecture, and execution of AI-driven solutions for the Revenue organization. Owns end-to-end applied AI function, from use-case identification to deployed, monitored, value-generating systems. Focuses on building production-grade AI agents and LLM-powered solutions embedded in workflows, making build vs. buy decisions, managing budget, and defining KPIs. Also responsible for data engineering foundations and developing proprietary models. Requires strong leadership and hands-on experience in applied AI and data engineering.

What you'd actually do

  1. Architect, build, and deploy production-grade AI agents and LLMpowered solutions embedded within daily internal Revenue workflows.
  2. Evaluate and execute build vs. buy vs. platform decisions — leveraging Nintex Automation CE and K2 where appropriate, partnering with Product and Engineering on shared infrastructure, and developing custom agentic systems using Claude and other foundation models for unique internal needs.
  3. Own the AI function’s budget, vendor relationships, and technical roadmap for internal applications.
  4. Define, track, and report key performance indicators (KPIs) that link AI investments to measurable outcomes in efficiency, revenue generation, and cost optimization; present progress to leadership on a monthly basis.
  5. Establish clear release standards for AI initiatives, ensuring each deployment has defined outcomes, an accountable workflow owner, and production monitoring in place.

Skills

Required

  • 12+ years building and deploying applied AI, machine learning, or data engineering systems in production environments
  • 5+ years leading high-performing technical teams or functions
  • Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field (or equivalent practical experience)

Nice to have

  • Advanced degree (MS or PhD) in Computer Science, Machine Learning, Data Science, or a related discipline.
  • Demonstrated experience designing and shipping LLM-powered and agentic systems to production at scale (prompt orchestration, retrieval, evaluation, and monitoring).
  • Background in mid-market and/or private equity–backed SaaS environments, with an understanding of their operating cadence, growth expectations, and capital discipline.
  • Hands-on experience with GTM / Revenue systems (e.g., CRM, CPQ, customer success and billing platforms) and the data they generate.
  • Experience with modern data platforms and tooling (e.g., cloud data warehouses/lakehouses, ELT/transformation, orchestration) and MLOps/LLMOps practices.
  • Familiarity with workflow automation / iPaaS platforms such as Nintex Automation CE, K2, or comparable tools.
  • Track record of partnering with Security, Legal, and Privacy functions to deploy AI responsibly and compliantly.

What the JD emphasized

  • production-grade AI agents
  • LLMpowered solutions
  • build vs. buy vs. platform decisions
  • custom agentic systems
  • production monitoring
  • AI-driven automation
  • develop and operationalize proprietary models
  • responsible, reliable, and scalable AI deployment
  • production-first delivery model
  • rapid iteration, production-first delivery

Other signals

  • build vs. buy decisions
  • production-first delivery model
  • measurable business outcomes
  • AI-driven automation across the full customer lifecycle
  • develop and operationalize proprietary models