Applied Responsible 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 workstreams, 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. Responsibilities Responsible AI Framework and Controls 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. Use Case Assessment and Delivery Enablement 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, oversight partners, auditors, and regulators - not just the teams that built them. Testing, Monitoring, and Technical Risk Assessment Help define fit‑for‑purpose evaluation and monitoring approaches for AI solutions, covering model performance, stability, explainability, fairness, robustness, human oversight effectiveness, data quality, and business outcome monitoring as appropriate to the use case. Design or guide evaluation approaches for generative AI solutions - including measures for groundedness and faithfulness, retrieval quality, task accuracy, harmful output risk, prompt and data‑handling vulnerabilities, and human review adequacy. Review testing and monitoring evidence and communicate clearly where results support implementation, where limitations must be formally documented, or where remediation or additional controls are required before progression. Collaborate with Applied AI and platform teams to embed governance requirements, evaluation workflows, monitoring and observability, and control evidence into repeatable delivery processes and enabling technology rather than treating them as separate activities. Risk Partner and Regulatory Readiness Collaboration Work with 1st line risk and 2nd line oversight partners - including Model Risk Management, Compliance, Legal, Privacy, Enterprise Risk, and Information Security - to align AI use case governance with the bank's broader risk and control framework. Support documentation, evidence collection, issue remediation, examination readiness, audit responses, and management reporting related to AI governance and responsible use. Contribute to policies, standards, procedures, training, and stakeholder communications that improve AI risk fluency and clarify responsibility for responsible AI delivery across the bank. Build trusted relationships and influence outcomes across business, technology, and risk teams without relying on direct authority - communicating risk clearly, understanding delivery pressures and constraints, and identifying practical paths forward. Process Improvement and Team Contribution Identify opportunities to streamline and selectively automate AI intake, control mapping, evidence tracking, risk assessment, and ongoing monitoring activities - while preserving appropriate accountability and the quality of effective challenge. Bring intellectual curiosity and informed judgment to new AI capabilities, vendor offerings, and business proposals; help the bank distinguish meaningful innovation from unnecessary complexity or unmanaged risk. Contribute to a collaborative, high‑accountability team culture and mentor business or technology stakeholders and less experienced practitioners through project work and shared learning. WHAT SUCCESS LOOKS LIKE Responsible AI requirements are translated into clear, practical, and repeatable delivery expectations - understood by the teams building AI solutions, not just the teams reviewing them. High‑value AI opportunities move efficiently through governance, with risks identified early, controls appropriately designed, and evidence ready for challenge and regulatory review. Business, technology, and risk partners view Enterprise AI as a trusted advisor that enables responsible adoption - not a checkpoint that sits at the end of the process and says no. The bank's posture on AI governance, monitoring, and control effectiveness is one it can confidently demonstrate to senior leadership, oversight functions, and regulators. Qualifications Bachelor's Degree and 6 years of experience in Data management, information technology, financial services, enterprise risk management, or related disciplines with leadership in AI/advanced analytics delivery. OR High School Diploma or GED and 10 years of experience in Data management, information technology, financial services, enterprise risk management, or related disciplines with leadership in AI/advanced analytics delivery. PREFERRED QUALIFICATIONS AND SKILLS Experience with Responsible AI, AI risk management, model governance, or model validation in financial services or another regulated environment. Prior experience in a model validation, independent review, or first‑line AI governance role is strongly preferred. Deep working knowledge of machine learning and generative AI lifecycle risks: use case intake and framing, solution evaluation, human oversight design, performance monitoring, third‑party AI risk, privacy, explainability, fairness, and regulatory‑grade documentation. Ability to interpret and operationalize model risk principles in emerging AI contexts - including the judgment required to apply SR 11-7 / SR 26-2 reasoning to generative AI use cases where model risk guidance does not directly prescribe an answer. Demonstrated ability to develop and implement generative AI evaluations and metrics, including fit‑for‑purpose measurement for solution quality, groundedness and faithfulness, retrieval performance, content safety, robustness, human oversight, and monitoring over time. Experience using or evaluating generative AI evaluation, monitoring, or observability tooling - such as Galileo, Fiddler AI, or comparable platforms - is a plus. Demonstrated ability to balance disciplined risk management with innovation enablement: designing proportionate governance approaches that maintain meaningful challenge and defensible evidence while enabling well‑designed solutions to progress efficiently. Excellent stakeholder management, executive communication, and influencing‑without‑authority skills; experience navigating and driving change across business, technology, and risk partners in a complex organization is highly valued.

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