AI Safety & Responsible/Governance AI Lead

AI Safety & Responsible/Governance AI Lead

Jersey City, NJ (Onsite Role)

Role purpose

Define and operationalize Responsible AI practices across the AI lifecycle. The role ensures AI systems are safe, fair, explainable, transparent, compliant, monitored, and aligned with enterprise values, model risk, legal, compliance, data governance, and audit expectations.

Primary ownership

• Responsible AI policy, control framework, risk taxonomy, governance workflows, and production-readiness criteria.
• AI risk assessments, impact assessments, safety evaluations, and post-production monitoring standards.
• Cross-functional alignment across engineering, product, legal, compliance, model risk, audit, cybersecurity, and data governance.

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, and ongoing monitoring.
• Develop safety and evaluation frameworks covering fairness, bias, explainability, transparency, robustness, privacy, hallucination, harmful outputs, and human oversight.
• Define guardrail requirements for LLMs, RAG systems, agentic workflows, and high-risk AI applications.
• Partner with model risk, legal, compliance, data governance, cybersecurity, and audit teams to align AI controls with enterprise expectations.
• Lead AI impact assessments, risk reviews, control assessments, readiness reviews, and remediation planning.
• Establish metrics and monitoring for bias indicators, safety violations, explainability gaps, harmful outputs, user feedback, and behavior drift.

Must-have

• 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, and unsafe automation.
• Ability to translate policy and regulatory expectations into practical product and engineering controls.

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, or Responsible AI playbooks.
• Familiarity with global AI governance frameworks, model validation practices, privacy regulation, and audit expectations.