AI Safety and AI Lead

AI Safety & Responsible AI Lead

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.

Client-specific emphasis:

• The organization is aiming to democratize AI responsibly; this role must support enterprise AI plus citizen 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.

Primary ownership:

• 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 over reliance.
• 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.

Must-have candidate profile:

• 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, over reliance, 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 experience:

• 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.

Initial screening questions:

• What Responsible AI framework have you implemented, and how was it operationalized?
• How do you classify AI use-case risk in a regulated enterprise?
• How would you govern KYC, credit underwriting, financial crime, or sanctions screening AI use cases?
• How do you govern citizen development through Copilot Studio, Power Apps, Power Automate, and Power BI?
• How do you evaluate and monitor hallucination, bias, fairness, explainability, and human oversight?
• How do you balance innovation speed with control expectations?