AI & Modeling Relations Manager

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• Serve as the single point of contact between the business unit and the AI Center of Excellence, managing intake of AI use cases, prioritization, and delivery timelines.
• Build and maintain a trusted working relationship with COE leadership, architects, data scientists, and ML engineers to ensure business priorities are represented in the COE roadmap.
• Translate business problems into well-formed AI use case briefs and translate COE technical capabilities and constraints back into business-friendly guidance.
• Facilitate regular governance/steering meetings, roadmap reviews, and escalation paths between the business unit and the COE.

• Ensure all AI initiatives sourced through the COE comply with enterprise responsible AI principles, model risk management standards, and data classification/privacy requirements.
• Partner with Legal, Privacy, Security, and Risk teams to support AI impact assessments, model documentation, and audit readiness.
• Maintain a current inventory of AI/ML models and tools in use within the business unit, including their approval and governance status.

• Partner with the COE to evaluate new AI platforms, foundation models, and third-party AI vendors (e.g., LLM providers, vector database platforms, AI agent frameworks) against business requirements.
• Coordinate proof-of-concept (POC) and pilot evaluations, defining success criteria, evaluation rubrics, and go/no-go recommendations.
• Stay current on the evolving AI vendor landscape (foundation model providers, AI copilots, agentic platforms) and advise the business on build-vs-buy decisions in partnership with the COE.

• Identify and prioritize high-value AI use cases within the business unit, in partnership with COE data scientists and solution architects.
• Drive adoption of COE-delivered AI capabilities (copilots, chatbots, predictive models, automation agents) through training, change management, and communication.
• Act as the business unit’s champion for AI literacy, helping teams understand what AI can (and cannot) responsibly do.

• Bachelor's Degree plus 5 years of related work experience OR Advanced degree with 3 years of related experience
• Acceptable areas of study include Finance, Economics, Mathematics. Statistics or related field
• 5+ years of experience in technology program/product management, vendor management, or a related cross-functional leadership role.
• Demonstrated working knowledge of AI/ML concepts and terminology, including large language models (LLMs), generative AI, machine learning, natural language processing (NLP), retrieval-augmented generation (RAG), prompt engineering, and agentic AI.
• Experience managing relationships with technical teams, centers of excellence, or shared services organizations.
• Strong communication skills with the ability to translate technical AI concepts for non-technical business stakeholders and vice versa.
• Key AI Literacy Expectations:
  • Foundation models & LLMs - GPT, Claude, Gemini, Llama - and how they're selected/deployed.
  • Generative AI vs. predictive/traditional ML vs. agentic AI.
  • Prompt engineering, fine-tuning, and retrieval-augmented generation (RAG).
  • Embeddings and vector databases.
  • MLOps/LLMOps, model monitoring, and model drift.
  • Responsible AI: bias, fairness, explainability, hallucination, guardrails.
  • AI governance and risk frameworks, data classification, and privacy-by-design.
  • Inference cost, token usage, and compute optimization.

• Experience working directly with or within an AI/ML Center of Excellence, data science team, or enterprise AI governance function.
• Familiarity with responsible AI/model risk frameworks (e.g., NIST AI RMF, ISO/IEC 42001) and AI governance practices.
• Working knowledge of MLOps/LLMOps concepts, model evaluation, fine-tuning, embeddings, and vector databases.
• Experience with the AI vendor and platform landscape (e.g., OpenAI, Anthropic, Google, Microsoft Copilot, AWS Bedrock) sufficient to support evaluation…