Enterprise AI Platform Lead

RSA provides trusted identity and access management for 12,000 organizations around the world, managing 25 million enterprise identities and providing secure, convenient access to millions of users. RSA specializes in empowering security-first organizations in financial services, healthcare, energy, technology services, and other industries to thrive in a digital world, delivering complete capabilities for modern authentication, access, lifecycle management, and identity governance. Whether in the cloud or on-premises, RSA connects people with the digital resources they depend on everywhere they live, work, and play.

For decades, RSA has pioneered many of the encryption, authentication, and identity federation technologies that still power the internet. And now RSA is transforming the industry yet again, paving the way for the future of digital identity through the RSA Unified Identity Platform; next-generation hybrid and cloud solutions; the first ever and only multi-functional, passwordless hardware authenticator; and a frictionless, mobile-optimized experience for the modern workforce. If you are self-motivated and looking for a fast-paced challenge doing something that truly matters, come join our winning team!  For more information, go to rsa.com.

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
  • Terraform
  • Platform 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:

• Bachelor's Degree in:
• • Computer Science
  • Information Technology
  • Engineering
  • Information Systems
• 7–10+ 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.

If you need a reasonable accommodation during the application process, please contact the RSA Talent Acquisition Team at rsa.global.talent.acquisition@rsa.com. RSA and its approved consultants will never ask you for a fee to process or consider your application for a career with RSA. RSA reserves the right to amend or withdraw any job posting at any time, including prior to the advertised closing date.