AI Safety & Responsible AI Lead

JOB SUMMARY Responsible AI / AI Governance / Model Risk / Ethical AI Governance Lead / Senior Manager or Director-level Specialist Responsible AI Lead, AI Governance Lead, AI Risk Lead, Model Governance Lead, AI Ethics Lead, AI Policy Lead, AI Safety Lead Responsible AI, AI governance, AI safety, model risk, model governance, AI ethics, fairness, bias, explainability, transparency, hallucination, guardrails, AI risk taxonomy, controls, AIRP, citizen development, Copilot Studio, Power Platform Define and operationalize Responsible AI practices across the AI lifecycle for AIRP and enterprise citizen-development initiatives. The role ensures AI systems are safe, fair, explainable, transparent, compliant, monitored, and aligned with enterprise values, model risk, legal, compliance, data governance, cybersecurity, and audit expectations. The organization is aiming to democratize AI responsibly; this role must support enterprise AI pl development through Microsoft Power Platform, Copilot Studio, Power Apps, Power Automate, and Power BI. Governance must be practical enough to support business AI use cases while satisfying banking, model risk, security, privacy, and audit controls. The candidate should be able to govern high-risk workflows such as KYC, credit underwriting, financial crime, and sanctions screening. Responsible AI policy, control framework, risk taxonomy, governance workflows, and production-readiness criteria for AIRP and citizen AI use cases. AI risk assessments, impact assessments, safety evaluations, model-risk alignment, and post-production monitoring standards. Cross-functional alignment across engineering, product, legal, compliance, model risk, audit, cybersecurity, data governance, and citizen-development enablement teams. Key Responsibilities Define Responsible AI standards, policies, procedures, risk-classification methods, and operating models for AI and GenAI initiatives. Establish governance processes for use-case intake, risk assessment, model review, approval workflows, deployment readiness, ongoing monitoring, and issue escalation. Develop safety and evaluation frameworks covering fairness, bias, explainability, transparency, robustness, privacy, hallucination, harmful outputs, human oversight, and overreliance. Define guardrail requirements for LLMs, RAG systems, agentic workflows, high-risk banking applications, and citizen-development solutions. Partner with model risk, legal, compliance, data governance, cybersecurity, audit, product, engineering, and business teams to align AI controls with enterprise expectations. Lead AI impact assessments, risk reviews, control assessments, readiness reviews, remediation planning, and AI incident escalation processes. Establish metrics and monitoring for bias indicators, safety violations, explainability gaps, harmful outputs, hallucination trends, user feedback, and behavior drift. Create governance playbooks and reusable control evidence for AIRP use cases and Power Platform / Copilot Studio citizen-development workflows. Required Qualifications Deep understanding of Responsible AI, AI ethics, model governance, model risk, explainability, fairness, privacy, safety, and enterprise risk management. Experience implementing AI governance or Responsible AI controls in production or enterprise environments. Understanding of LLM-specific risks such as hallucination, bias, toxicity, prompt injection, data leakage, overreliance, unsafe automation, and human oversight gaps. Ability to translate policy and regulatory expectations into practical product, engineering, operating, and audit controls. Experience working with cross-functional risk, compliance, legal, security, data, audit, product, and engineering stakeholders. Ability to define controls that scale across centralized AI platforms and distributed citizen-development adoption. Preferred Qualifications Experience in banking, insurance, fintech, consulting, regulatory risk, model risk management, technology governance, or data governance. Experience building AI risk taxonomies, control libraries, governance operating models, Responsible AI playbooks, or model-risk-aligned review processes. Familiarity with Power Platform, Microsoft Copilot Studio, Power Apps, Power Automate, Power BI, global AI governance frameworks, model validation practices, privacy regulation, and audit expectations. Education: Masters Degree