AI Governance Arcitect

Fractal Analytics•New Jersey, NJ

1d•$180,000 - $200,000

About The Position

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. The ideal candidate will combine expertise in enterprise architecture, AI/Agentic AI governance, cloud platforms, risk management, and Financial Services regulations, with experience working across technologies such as Claude LLM, OpenAI Custom GPTs, AWS, AI Agents, MCP Servers, and enterprise AI applications.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Information Systems, or a related field.
10+ years of experience in Enterprise Architecture, Technology Governance, AI/ML Platforms, or Risk Management.
5+ years of experience designing and implementing AI Governance or Model Governance frameworks.
Demonstrated experience building enterprise inventories, asset registries, governance repositories, or catalog solutions.
Strong knowledge of: AI Governance, Responsible AI, AI Risk Management, Model Lifecycle Management, Agentic AI Governance, Enterprise Architecture.
Experience working with: Claude LLM, OpenAI / Custom GPT implementations, AWS Cloud Services (Bedrock, SageMaker, Lambda, IAM, etc.).
Hands-on experience defining evaluation methodologies for LLMs, AI agents, and GenAI applications.
Experience defining governance controls across AI agents, applications, and enterprise AI platforms.
Strong understanding of data privacy, cybersecurity, and regulatory requirements within Financial Services.
Excellent communication, stakeholder management, and leadership skills.

Nice To Haves

Experience building an enterprise AI Registry from concept through implementation.
Experience governing Agentic AI ecosystems, MCP servers, AI marketplaces, or AI service catalogs.
Familiarity with model risk management (MRM) frameworks used within Financial Services organizations.
Knowledge of banking, asset management, wealth management, capital markets, or insurance domains.
Experience with AI observability, monitoring, evaluation, and governance tooling.
AWS Solutions Architect, AWS Machine Learning Specialty, TOGAF, or equivalent professional certifications.
Experience supporting enterprise AI transformation initiatives and governance operating models at scale.
Understanding of emerging industry standards related to AI governance, responsible AI, and agent governance.

Responsibilities

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 Agents, MCP Servers, AI Applications, AI Tools, LLMs 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, and 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 LLM, OpenAI Custom GPTs, AWS Bedrock, AWS AI/ML Services, AI Agent Platforms, MCP-based ecosystems.
Define architectural patterns for secure, scalable, and compliant AI deployments.
Establish governance controls for model access, prompt management, data usage, and external AI integrations.
Conduct AI governance assessments and risk reviews.
Support regulatory compliance initiatives specific to Financial Services.
Develop governance dashboards, audit trails, compliance reporting, and executive-level metrics.
Maintain comprehensive documentation supporting regulatory, internal audit, and model risk requirements.
Act as the enterprise subject matter expert for AI Governance and AI Architecture.
Drive adoption of governance processes across business and technology teams.
Present governance roadmaps, risk assessments, and recommendations to senior leadership and governance committees.