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Fractal Analytics

Enterprise AI solutions for decision making

AI Governance Architect

Technology & Systems ConsultingConfirmed live in the last 24 hours
Full-timeNew Jersey, USAIn-person

About the Job

Requirements

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

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, and AI services and APIs.
  • Ensure the registry serves as a discovery platform for users to identify approved AI assets and capabilities and a 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, and 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, and 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.

Desired Qualifications

  • 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.

About the Company

Fractal Analytics

Enterprise AI solutions for decision making

Fractal Analytics provides AI-powered analytics and decision-support solutions for large enterprises, including Fortune 500 companies. Its products combine data science, machine learning, and natural language processing to extract insights, automate tasks, and enable conversations: Crux Intelligence (BI), Senseforth.ai (conversational AI), Eugenie.ai (sustainability), and Asper.ai (revenue growth). The company differentiates itself with a broad, enterprise-focused product portfolio and a global delivery footprint of 4,000+ employees across 16 locations, plus industry analyst recognition and workplace awards. Its goal is to power every human decision in the enterprise by bringing AI, engineering, and design together to help leading companies make better decisions.

Company size
5,001-10,000
Company stage
IPO
Headquarters
New York City, New York
Founded
2000

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