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: MCP (Model Context Protocol) implementations API integrations Agent-to-system communication External AI service integrations Enterprise tool connectivity
• Establish secure and governed integration patterns between AI platforms and enterprise systems.

• Establish operational excellence across the AI ecosystem.
• • Implement monitoring and observability for: AI agents Platform health Model utilization System performance Security events Usage trends User adoption
  • Develop: Runbooks Incident response procedures Support processes & Escalation workflows
  • Lead troubleshooting efforts related to: Agent failures Integration issues Model outages Performance degradation & Cost anomalies
• Act as a trusted advisor for AI initiatives across the organization.
• • Responsibilities include:
  • • Supporting business use-case development
    • Designing platform standards
    • Providing best practices and templates
    • Educating technical teams
    • Enabling controlled self-service AI development
• Balance innovation with governance and operational excellence.

Skills:

• AI Platforms: Microsoft Copilot Copilot Studio Azure AI Foundry Azure OpenAI Anthropic Claude Codex & Enterprise AI Platforms
• AI Technologies: Large Language Models (LLMs) Agentic AI AI Agents Prompt Engineering RAG (Retrieval-Augmented Generation) Model Evaluation AI Governance MCP (Model Context Protocol)
• Cloud & Platform Engineering: Microsoft Azure AWS (Preferred) Kubernetes Docker TerraformPlatform Engineering & Infrastructure as Code (IaC)
• Security & Identity: Microsoft Entra ID OAuth SAML RBAC Conditional Access & Enterprise Security Architecture
• Development & Automation: Python PowerShell REST APIs GitHub Azure DevOps CI/CD Pipelines & Power Platform

Education & Experience:

• Bachelors Degree in: Computer Science Information Technology Engineering & Information Systems
• 710 years of experience in Enterprise Technology Infrastructure Cloud Engineering Platform Engineering or Solution Architecture.
• 3 years of experience implementing or managing AI GenAI or Large Language Model platforms.
• Experience designing and operating enterprise SaaS platforms.
• Experience leading technical initiatives across multiple teams.
• Experience working within regulated enterprise environments.

RSA is committed to the principle of equal employment opportunity for all employees and applicants for employment and to providing employees with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race color and any other category protected by applicable country law.

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