Applied AI Lead

Overview

Responsible AI in banking is not a constraint imposed from the outside, and it is not primarily about abstract ethics or headline‑grabbing model safety debates. In a regulated financial institution, AI introduces risk across model performance, customer impact, regulatory compliance, legal obligations, privacy, cybersecurity, third‑party dependency, operational resilience, data governance, and reputation. Managing those risks well is exactly what allows the bank to move forward with confidence — not despite governance, but because of it.

This role operates as a "1.5 Line of Defense" — a concept that is distinctive and important. The Applied Responsible AI Lead works between the 1st Line (data scientists, AI engineers, technology delivery teams, and business use case owners) and the 2nd Line (Model Risk Management, Compliance, Legal, Privacy, Fair and Responsible Banking, Information Security, and Enterprise Risk). The role is neither a rubber stamp nor a late‑stage blocker.

It is a partner that gets in early, asks the right questions, and helps teams design AI initiatives that are well‑documented, well‑controlled, and defensible from the start.

Think of this role as both a brake and a lever. The brake: applying structured challenge where a use case creates material risk or lacks adequate controls. The lever: building the frameworks, reusable patterns, evaluation approaches, and practical guidance that allow well‑designed solutions to move efficiently from idea to implementation. The goal is not to slow AI down — it is to make sure the AI that moves forward is AI the bank can stand behind.

This is a senior professional individual contributor role. The successful candidate will independently lead complex governance work streams, bring structured judgment to ambiguous and emerging questions, communicate clearly across technical and non‑technical stakeholders, and help establish repeatable approaches that scale without losing rigor.

• Support the design, operationalization, and continuous improvement of the bank's Responsible AI framework — including governance processes, control expectations, review pathways, documentation standards, and AI use case patterns — for both machine learning and generative AI solutions.
• Translate Responsible AI principles and evolving regulatory expectations (including SR 11-7 / SR 26-2, OCC guidance, and emerging GenAI‑specific standards) into practical, implementable requirements for data scientists, engineers, business owners, platform teams, and vendors.
• Develop reusable risk assessment approaches, control mappings, AI pattern blueprints, evidence checklists, and monitoring expectations that promote consistency and scalability across the enterprise.
• Establish a proportionate, risk‑tiered approach to governance that differentiates appropriately between lower‑risk analytics, material AI/ML models, generative AI applications, and higher‑risk customer‑facing use cases — applying the right level of rigor to each without imposing uniform overhead across all.

• Partner with business and technology teams from early intake through solution design, implementation, testing, deployment, and post‑production monitoring — identifying risks early and recommending practical, proportionate mitigations.
• Perform or support risk assessments and effective challenge for internally developed and third‑party AI solutions across the use case spectrum: traditional AI/ML, generative AI, document intelligence, decision support, content generation, summarization, retrieval‑augmented generation, and emerging AI applications.
• Evaluate whether proposed AI use cases have sufficiently clear business purpose, accountable ownership, human oversight provisions, appropriate data usage, adequate testing plans, defined controls, monitoring expectations, and the evidence needed to proceed through the appropriate governance pathway.
• Guide teams in documenting use cases, assumptions, limitations, controls, test outcomes, risk decisions, and implementation conditions in a manner that is clear to senior management,…