Principal Data/AI Enterprise Architect

Company Description

It all started in sunny San Diego, California in 2004 when a visionary engineer, Fred Luddy, saw the potential to transform how we work. Fast forward to today — ServiceNow stands as a global market leader, bringing innovative AI-enhanced technology to over 8,100 customers, including 85% of the Fortune 500®. Our intelligent cloud-based platform seamlessly connects people, systems, and processes to empower organizations to find smarter, faster, and better ways to work. But this is just the beginning of our journey. Join us as we pursue our purpose to make the world work better for everyone.

Job Description

About Digital Technology:

We’re not yesterday’s IT department, we're Digital Technology. The world around us keeps changing and so do we. We’re redefining what it means to be IT with a mindset centered on transformation, experience, AI-driven automation, innovation, and growth.

We’re all about delivering delightful, secure customer and employee experiences that accelerate ServiceNow’s journey to become the defining enterprise software company of the 21st century. And we love co-creating, using, and highlighting our own products to do it. 

Ultimately, we strive to make the world work better for our employees and customers when you work in ServiceNow Digital Technology, you work for them. 

Job Overview

The Principal Data & AI Enterprise Architect is a senior leadership role responsible for defining, designing, and driving the data and artificial intelligence (AI) architecture across the enterprise. This role ensures the strategic alignment of data, AI, and machine learning (ML) initiatives with business goals, focusing on scalability, security, and innovation.  This role requires a visionary leader who can bridge the gap between business needs and technical capabilities.  The Principal Architect will collaborate with cross-functional teams to integrate AI-driven solutions that enhance decision-making, operational efficiency, and customer experience.

Key Responsibilities

• Enterprise Data & AI Strategy:
  • Develop and lead the enterprise-wide data and AI architecture strategy, ensuring alignment with business objectives.
  • Identify opportunities to leverage AI and machine learning technologies to optimize business processes and outcomes.
  • Drive the adoption of emerging technologies in AI, machine learning, and data analytics to keep the organization ahead of technological trends.

• Architectural Design & Oversight:
  • Design and implement data and AI frameworks, ensuring scalability, security, and efficiency.
  • Provide architectural oversight for AI/ML projects, including platform selection, model development, deployment, and integration.
  • Ensure best practices in data governance, AI ethics, privacy, and compliance are maintained.

• Collaboration & Leadership:
  • Collaborate with business leaders, data scientists, data engineers, and software development teams to define and deliver AI-driven solutions.
  • Guide teams in adopting new AI technologies, platforms, and methodologies.
  • Present complex AI strategies and roadmaps to executive leadership, articulating the value and potential impact on the business.

• Innovation & Research:
  • Stay up to date with the latest advancements in AI, machine learning, and data analytics technologies.
  • Lead R&D initiatives to evaluate new tools, platforms, and techniques that can enhance the organization’s data and AI capabilities.
  • Drive innovation in the application of AI for predictive analytics, automation, and operational intelligence.

• Governance & Compliance:
  • Establish and enforce AI governance policies to ensure ethical AI use, transparency, and accountability.
  • Ensure all data and AI architectures comply with regulatory and security requirements, including GDPR, CCPA, and other data privacy laws.

• Performance Monitoring & Optimization:
• • Define key performance metrics (KPIs) for AI systems and solutions.
  • Monitor AI models for performance, accuracy, and fairness, making recommendations for improvement.
  • Ensure the AI models are explainable, reproducible, and maintainable.

Qualifications

Required Qualifications

• Education:
  • Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
  • Master’s or Ph.D. in AI, Machine Learning, Data Science, or related fields is preferred.
• Experience:
  • 10+ year