What are the responsibilities and job description for the AI Safety & Responsible AI Lead position at VDart, Inc.?
Title: AI Safety & Responsible AI Lead
Location: Jersey City, NJ(Hybrid)
Type: Contract
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 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, 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.