AI Governance Arcitect

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We are seeking an experienced AI Governance Architect to establish and operationalize enterprise-wide AI governance capabilities within a leading Financial Services organization. This role will be responsible for defining AI governance frameworks, building and managing an enterprise AI Registry, governing AI agents and AI applications, and designing evaluation frameworks to ensure responsible, compliant, and scalable AI adoption.

• Define and implement the enterprise AI Governance strategy, operating model, standards, and control frameworks.
• Establish governance scope covering AI models, AI agents, GenAI applications, MCP servers, AI tools, prompts, datasets, and supporting infrastructure.
• Create policies, lifecycle controls, approval workflows, and risk management processes for AI assets.
• Partner with Risk, Compliance, Security, Legal, Data, and Business stakeholders to operationalize AI governance.

• Define the vision, architecture, and operating model for an enterprise-wide AI Registry.
• Design and build a centralized inventory encompassing: AI AgentsMCP ServersAI ApplicationsAI ToolsLLMs and Foundation Models Prompts and Prompt Libraries AI Services and APIs
• Ensure the registry serves a dual purpose:
  Discovery Platform for users to identify approved AI assets and capabilities.
  
  Governance Platform for managing ownership, compliance, risk, and lifecycle controls.
• Define metadata standards, ownership models, classification schemes, and governance workflows.
• Establish inventory management processes across business units and technology functions.

• Define governance standards and controls for Agentic AI and autonomous workflows.
• Establish requirements around auditability, explainability, human oversight, monitoring, and accountability.
• Develop governance guardrails for multi-agent ecosystems and interactions with enterprise systems.
• Create standards for onboarding, certification, and production deployment of AI agents.

• Design and implement an enterprise AI Evaluation Framework covering:
  Functional testing
  
  Accuracy and performance validation
  
  Hallucination testing
  
  Risk and compliance validation
  
  Security testing
  
  Prompt testing and evaluation
  
  Bias and fairness assessment
  
  Agent behavior validation
  
  Business outcome measurement
• Define what must be tested at each stage of the AI lifecycle.
• Establish evaluation metrics, benchmarks, acceptance criteria, and approval workflows.
• Enable continuous monitoring and post-production governance processes.

• Define governance standards for AI solutions leveraging:
  Claude LLMOpenAI Custom GPTsAWS Bedrock AWS AI/ML Services AI Agent PlatformsMCP-based ecosystems
• Define architectural patterns for secure,…