AI Safety & AI Lead Jersey , NJ; Hybrid – Onsite

AI Safety & Responsible AI Lead

Location:

Jersey City, NJ (Hybrid – 4 Days Onsite)

The AI Safety & Responsible AI Lead will define, implement, and operationalize Responsible AI practices across the entire AI lifecycle for AIRP and enterprise citizen-development initiatives. This role is responsible for ensuring that AI and Generative AI systems are safe, fair, explainable, transparent, and compliant, secure, continuously monitored, and aligned with enterprise values, model risk management, legal, compliance, cybersecurity, data governance, privacy, and audit expectations.

As the organization continues to democratize AI responsibly, this role will establish practical governance frameworks that enable enterprise AI adoption while supporting citizen developers using Microsoft Power Platform, Copilot Studio, Power Apps, Power Automate, and Power BI. Governance frameworks must balance innovation with the stringent requirements of banking regulations, model risk management, security, privacy, compliance, and audit controls, including oversight of high-risk AI use cases such as KYC, credit underwriting, financial crime, AML, and sanctions screening.

The successful candidate will own the enterprise Responsible AI policy, AI governance framework, AI risk taxonomy, control framework, governance workflows, and production-readiness criteria for both enterprise AI and citizen-developed AI solutions. They will lead AI risk assessments, impact assessments, safety evaluations, model risk alignment, deployment readiness reviews, and post-production monitoring, while driving cross-functional collaboration across engineering, product management, legal, compliance, model risk, audit, cybersecurity, data governance, and citizen-development enablement teams.

• Define and implement enterprise Responsible AI standards, policies, procedures, governance frameworks, operating models, control libraries, and AI risk-classification methodologies for AI and Generative AI initiatives.
• Establish end-to-end governance processes covering AI use-case intake, risk assessments, impact assessments, model reviews, approval workflows, deployment readiness, production governance, ongoing monitoring, issue management, and escalation procedures.
• Develop comprehensive AI safety and evaluation frameworks addressing fairness, bias, explainability, transparency, robustness, privacy, security, hallucinations, harmful outputs, toxicity, human oversight, over reliance, and responsible AI principles.
• Define and implement governance guardrails for Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, agentic AI workflows, autonomous AI agents, high-risk banking applications, and citizen-developed solutions built on Microsoft Power Platform and Copilot Studio.
• Partner closely with Model Risk Management, Legal, Compliance, Cybersecurity, Data Governance, Audit, Product Management, Engineering, and Business stakeholders to translate regulatory requirements and enterprise policies into practical AI controls, operational workflows, engineering standards, and audit evidence.
• Lead AI impact assessments, model risk reviews, governance reviews, control assessments, production readiness evaluations, remediation planning, and AI incident response and escalation activities.
• Define enterprise metrics, KPIs, and continuous monitoring processes for bias indicators, fairness metrics, explainability gaps, safety violations, hallucination trends, harmful outputs, user feedback, model behavior drift, and operational risk.
• Develop reusable Responsible AI governance playbooks, AI control evidence, governance templates, review checklists, and implementation standards to support AIRP initiatives and Power Platform/Copilot Studio-based citizen development across the enterprise.
• Ensure enterprise AI governance enables responsible innovation while meeting banking, regulatory, privacy, security, model risk, and audit requirements, allowing AI solutions to scale safely across both centralized AI platforms and distributed citizen-development environments.

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,…