Staff VP AI Technology

Anticipated End Date:

2025-08-06

Position Title:

Staff VP AI Technology

Job Description:

Staff VP AI Technology

Location:   May be located in any Elevance Health PulsePoint office preferably in Indianapolis, IN, Atlanta, GA, Mason, OH, Richmond, VA, Norfolk, VA or Woodland Hills, CA.

This role requires associates to be in-office at least 3 days per week, fostering collaboration and connectivity, while providing flexibility to support productivity and work-life balance. This approach combines structured office engagement with the autonomy of virtual work, promoting a dynamic and adaptable workplace. Alternate locations may be considered.  Please note that per our policy on hybrid/virtual work, candidates not within a reasonable commuting distance from the posting location(s) will not be considered for employment, unless an accommodation is granted as required by law.

Summary

Responsible for overseeing the AI strategic initiatives that optimize the value of artificial intelligence across the enterprise; enabling multiple business needs including improving service; and driving adoption of AI and analytics across enterprise.

Position Responsibilities

AI Services Strategy & Roadmap

• Define Strategic Direction: Develop and refine the AI services roadmap, ensuring alignment with the organization’s vision and business priorities.  
• Leverage Platform Capabilities: Identify opportunities to reuse and extend the organization’s existing AI platform modules, components, and frameworks in new services and solutions.  
• Market & Innovation Insights: Maintain awareness of AI trends, tools, and best practices to continuously evolve the service portfolio.

Collaboration with AI Engineering & Platform Teams

• Seamless Integration: Partner with the AI Engineering team to understand the architecture, design patterns, and reusable modules of the AI platform, incorporating them effectively into client or internal solutions.  
• Feedback Loop: Provide structured feedback to AI Engineering on platform usability, performance, and feature requests to ensure continuous improvement of reusable components.  
• Standardization & Governance: Champion best practices for model deployment, MLOps, versioning, and quality assurance, ensuring consistent standards across all AI services.

Delivery & Project Oversight

• Project Coordination: Oversee multiple AI service engagements concurrently, from requirements gathering and scoping through development, testing, deployment, and post-launch monitoring.  
• Resource Allocation: Align cross-functional teams—data scientists, ML engineers, solution architects, and business analysts—to project demands, leveraging centralized AI platform assets for efficiency.  
• Quality & Compliance: Implement standardized processes (e.g., agile, MLOps frameworks) and adhere to data privacy, security, and regulatory guidelines for all AI deliverables.

Ecosystem Integration & Enablement

• Enterprise Alignment: Work with IT, Product, and Data teams to integrate AI solutions into the broader technology stack (data pipelines, APIs, microservices, DevOps workflows, etc.).  
• Technology Evangelism: Serve as an internal advocate for AI-driven solutions, highlighting the benefits of adopting reusable AI platform capabilities across business functions.  
• Scalability & Reliability: Ensure deployed AI services are scalable, resilient, and performant within the organization’s production environment.

Team Leadership & Development

• Build & Mentor Teams: Recruit, mentor, and guide a diverse, high-performing AI Services team, fostering an environment of continuous learning and innovation.  
• Skill Development: Drive ongoing technical and professional development initiatives to keep teams at the forefront of AI advancements and platform best practices.  
• Culture & Collaboration: Promote open communication, knowledge sharing, and collaboration between AI Services, AI Engineering, and other organizational teams.

Stakeholder Management & Business Impact

• Executive Partnering: Engage with senior leadership to define, prioritize, and align AI service initiatives with strategic business goals.  
• Client & Business Unit Relations: Collaborate with external clients or internal departments to understand requirements, tailor AI solutions, and deliver measurable outcomes.  
• Value Measurement: Define and track KPIs (e.g., adoption rate, cost savings, revenue uplift, model performance) to demonstrate the tangible business impact of AI services.

Financial