Enterprise AI Platform Lead

Principal Responsibilities:

• Serve as the primary owner of the enterprise AI platform ecosystem.
• Define and maintain:
• • Enterprise AI strategy and roadmap
  • AI platform architecture standards
  • Platform governance framework
  • Operational standards and procedures
  • Platform lifecycle management processes
  • AI adoption and enablement strategy
• Drive enterprise-wide adoption while ensuring security, reliability, scalability, and compliance.
• Lead the design, implementation, and support of enterprise AI solutions.
• Responsibilities include:
• • Building AI agents and copilots
  • Implementing RAG architectures
  • Developing enterprise AI integrations
  • Designing multi-agent solutions
  • Implementing AI orchestration patterns
  • Integrating AI with enterprise applications
  • Building reusable AI services and frameworks
• Remain actively involved in technical implementation, troubleshooting, and architecture reviews.
• Design and manage the complete lifecycle of enterprise AI agents.
• Establish standards for:
• • Agent development
  • Agent testing
  • Security reviews
  • Approval processes
  • Production deployment
  • Change management
  • Retirement and decommissioning
• Define governance controls for:
• • Agent ownership
  • Prompt management
  • Knowledge source management
  • Connector usage
  • Model selection
  • Version control
• Manage enterprise usage of AI models across multiple platforms.
• • Including:
  • • Azure OpenAI Models
    • Claude Models
    • Codex
    • Enterprise-approved LLMs
    • Future approved AI providers
  • Define:
  • • Approved models
    • Restricted models
    • Model usage policies
    • Model selection guidelines
    • Performance and quality standards
  • Continuously optimize:
  • • AI response quality
    • Cost efficiency
    • Model performance
    • Scalability
    • User experience
• Own AI platform financial governance and optimization.
• • Responsibilities include:
  • • AI consumption monitoring
    • Budget forecasting
    • License optimization
    • Credit management
    • Token management
    • Cost allocation and reporting
  • Implement:
  • • Usage quotas
    • Budget controls
    • Consumption policies
    • Chargeback and showback mechanisms
    • Cost optimization frameworks
  • Monitor and investigate:
  • • Cost anomalies
    • Token spikes
    • Excessive model consumption
    • Resource waste
• Partner with Security, Privacy, Risk, Compliance, and Legal teams to establish enterprise AI controls.
• • Implement:
  • • AI governance policies
    • Data Loss Prevention (DLP)
    • Role-based access controls
    • Environment segregation
    • Data classification enforcement
    • Audit and monitoring controls
  • Ensure:
  • • Responsible AI usage
    • Regulatory compliance
    • Enterprise policy compliance
    • Secure handling of business data
    • Risk management and mitigation
• Design integration architectures between AI platforms and enterprise applications.
• • Examples include:
  • • Salesforce
    • Netsuite
    • Jira
    • SharePoint
    • Microsoft 365
    • Dataverse
    • ERP platforms
    • Knowledge management systems
    • Internal APIs and business services
• Develop reusable integration patterns that support secure and scalable AI adoption.

• Define enterprise connectivity standards for AI systems.
• • Design and manage: