Vice President of Artificial Intelligence

Job Description: Vice President, Artificial Intelligence (AI)

Location: United States (Fully Remote)

Travel: Quarterly on-site visits (East Coast)

Compensation: $375,000 -- $410,000 base salary annual bonus equity/stock

Company Overview

A Fortune 200 organization within the utilities sector is undergoing a large-scale digital and operational transformation, with Artificial Intelligence positioned as a core strategic pillar. The company is investing significantly in data, advanced analytics, and AI-driven capabilities to modernize infrastructure, improve reliability, enhance customer experience, and optimize enterprise performance.

Role Overview

The Vice President of Artificial Intelligence will serve as the senior-most AI leader in the organization, responsible for defining, operationalizing, and scaling AI capabilities across the enterprise. This role requires a unique blend of strategic vision, technical depth, and operational execution.

You will lead the development of enterprise AI platforms, embed AI into critical business workflows, and ensure that AI investments deliver measurable business value. This role reports into executive leadership and will have high visibility across the C-suite and Board.

Key Responsibilities

Enterprise AI Strategy & Vision

• Define and continuously evolve a multi-year AI strategy aligned with corporate priorities and transformation initiatives
• Identify and prioritize high-impact AI use cases across operations, grid/asset management, customer experience, finance, and corporate functions
• Establish a clear value realization framework, linking AI initiatives to ROI, cost savings, and operational improvements
• Act as the organization's thought leader on AI trends, including generative AI, automation, and emerging technologies

AI Productization & Execution

• Lead the end-to-end lifecycle of AI solutions, including ideation, experimentation, development, deployment, and scaling
• Transition AI initiatives from pilot stages to production-grade, enterprise-wide solutions
• Ensure robust MLOps practices, including CI/CD pipelines, model monitoring, retraining, and performance optimization
• Drive adoption of AI products by embedding them into core business processes and decision-making workflows

Organizational Leadership & Talent Strategy

• Build, lead, and scale a high-performing AI organization, including data science, machine learning engineering, MLOps, and applied research teams
• Define operating models for centralized vs. federated AI capabilities across business units
• Recruit top-tier AI talent and develop internal capabilities through training, mentorship, and upskilling programs
• Foster a culture of innovation, accountability, and continuous improvement

Data & Technology Architecture

• Partner with data and technology leadership to define the enterprise AI/ML architecture and data strategy
• Oversee selection and implementation of AI platforms, tools, and frameworks (cloud-based and hybrid environments)
• Ensure scalable, secure, and high-quality data pipelines to support advanced analytics and machine learning
• Drive integration of AI capabilities into existing enterprise systems (ERP, CRM, operational systems, etc.)

Governance, Risk, & Responsible AI

• Establish enterprise-wide AI governance frameworks, including model validation, auditability, and lifecycle management
• Develop and enforce responsible AI standards addressing bias, fairness, explainability, and ethical considerations
• Ensure compliance with regulatory, legal, cybersecurity, and data privacy requirements
• Partner with risk and compliance teams to proactively manage AI-related risks

Cross-Functional Collaboration & Stakeholder Engagement

• Partner with executive leadership and business unit heads to align AI initiatives with strategic priorities
• Translate business challenges into AI-driven solutions with clear, measurable outcomes
• Act as a key liaison between technical teams and non-technical stakeholders
• Manage external partnerships with technology vendors, consulting firms, startups, and academic institutions

Performance Measurement & Value Realization

• Define KPIs and success metrics for AI initiatives (e.g., cost reduction, operational efficiency, revenue impact, reliability improvements)
• Establish dashboards and reporting mechanisms to track performance and communicate value to leadership
• Continuously refine AI investments based on business outcomes and evolving priorities

Required Qualifications

Experience

• 12--15 years of experience in artificial intelligence, machine learning, data science, or advanced analytics
• 7 years in senior leadership roles (Director, VP, or equivalent) with enterprise-level scope
• Proven track record of successfully deploying AI/ML solutions at scale in complex organizations
• Experience managing large, cross-functional teams and budgets

Technical Expertise

• Deep understanding of AI/ML methodologies, including:
  • Predictive modeling, optimization, and statistical analysis
  • Natural Language Processing (NLP) and generative AI (LLMs)
  • Computer vision and/or time-series forecasting
• Strong experience with cloud platforms (AWS, Azure, or GCP) and modern data ecosystems
• Familiarity with MLOps, data engineering, and model lifecycle management best practices

Leadership & Business Acumen

• Strong executive presence with the ability to influence C-suite and Board-level stakeholders
• Demonstrated ability to translate technical capabilities into tangible business value
• Experience leading large-scale transformation initiatives and driving organizational change
• Exceptional communication skills, with the ability to simplify complex concepts

Preferred Qualifications (Not Required)

• Experience in regulated or asset-intensive industries (e.g., utilities, energy, manufacturing, infrastructure, financial services)
• Exposure to operational optimization, predictive maintenance, or large-scale infrastructure analytics
• Experience implementing enterprise AI governance or responsible AI frameworks

What Success Looks Like (First 12--24 Months)

• Established a clear, enterprise-wide AI strategy with executive alignment
• Successfully scaled multiple AI use cases from pilot to production delivering measurable ROI
• Built a high-performing AI organization with strong talent retention and engagement
• Implemented robust AI governance and MLOps frameworks
• Demonstrated meaningful impact on operational efficiency, cost optimization, and customer outcomes

Why Join

• Opportunity to define and lead AI at Fortune 200 scale
• High-impact role with direct influence on enterprise strategy
• Significant investment in AI, data, and digital transformation
• Competitive executive compensation with long-term equity upside
• Flexible, fully remote work environment with periodic in-person collaboration