Website:
visarapartners.com
Job details:
Job Description: AI Policy & Governance Specialist
Location - Bangalore,Mumbai and Gurugram,
Experience - 5 to 10 yrs.
Role Overview
Design, implement, and operationalize enterprise-wide AI governance, policy, and responsible AI frameworks across the AI/ML, GenAI, and Agentic AI lifecycle. This role acts as a primary advisor on AI policy development, regulatory alignment, model governance, and responsible AI adoption, ensuring AI systems are deployed in a compliant, transparent, and risk-aware manner.
Required Skills
AI Policy & Governance Frameworks
Policy Development: Develop and maintain enterprise AI policies, standards, and control frameworks covering AI/ML, GenAI, and Agentic AI use cases across the full system lifecycle.
Governance Operating Model: Establish governance structures, decision forums, escalation paths, and accountability models for AI system design, approval, deployment, and monitoring.
Control Framework Alignment: Map AI governance controls to established frameworks and standards including NIST AI RMF, ISO/IEC 42001, OECD AI Principles, and relevant internal enterprise risk standards.
Regulatory Compliance & Responsible AI
Regulatory Alignment: Interpret and operationalize emerging AI regulations and guidance including the EU AI Act, privacy requirements, sector-specific regulations, and cross-border data governance considerations.
Responsible AI Implementation: Define and embed responsible AI principles including fairness, explainability, transparency, accountability, safety, and human oversight into governance processes and technology delivery models.
Risk Classification: Develop AI use-case classification models to identify risk levels, prohibited use cases, high-risk use cases, and required controls based on regulatory and enterprise requirements.
Model Governance & Lifecycle Oversight
Lifecycle Governance: Establish review and approval controls across data sourcing, model development, validation, deployment, change management, monitoring, and retirement.
Inventory & Documentation: Maintain enterprise AI asset inventories, model documentation standards, use-case records, decision logs, and governance evidence for audit and regulatory readiness.
Third-Party Oversight: Govern third-party AI tools, foundation models, vendors, and service providers through structured assessment, approval, and ongoing monitoring processes.
Risk Management & Assurance
Risk Assessments: Lead AI risk assessments covering legal, compliance, ethical, operational, privacy, and security dimensions across AI solutions and business use cases.
Controls Assurance: Define governance assurance mechanisms, policy adherence reviews, control testing, issue tracking, and remediation oversight for AI deployments.
Human Oversight: Design governance requirements for human-in-the-loop, human-on-the-loop, and exception-based oversight for high-impact and agentic AI systems.
Stakeholder Engagement & Change Enablement
Executive Advisory: Advise senior stakeholders on AI risk posture, compliance exposure, governance maturity, and policy decisions.
Cross-Functional Coordination: Partner with legal, privacy, compliance, risk, security, data science, and engineering teams to align governance requirements with delivery realities.
Awareness & Enablement: Develop governance playbooks, policy guidance, training content, and operating procedures to drive consistent enterprise adoption.
Key Responsibilities
Policy Leadership: Define, implement, and continuously improve enterprise AI governance policies, standards, and procedures.
Governance Frameworks: Build and operationalize compliance-ready AI governance frameworks aligned to NIST AI RMF, ISO/IEC 42001, and the EU AI Act.
Risk Oversight: Lead AI use-case intake, risk classification, control review, and governance approval processes for AI/ML, GenAI, and Agentic AI initiatives.
Regulatory Readiness: Support regulatory response, audit readiness, governance evidence collection, and control traceability across AI programs.
Responsible AI Enablement: Ensure fairness, transparency, accountability, and human oversight principles are embedded into AI operating models and delivery lifecycles.
Third-Party Governance: Assess and govern external AI platforms, models, and providers to ensure policy compliance and acceptable risk posture.
Cross-Functional Partnership: Work closely with legal, privacy, compliance, enterprise risk, data, and engineering teams to enable responsible and compliant AI adoption.
Practice Innovation: Build reusable governance templates, assessment methods, control libraries, and policy accelerators to support scalable client delivery.
Qualifications
Education & Experience
Degree: Bachelor’s or master’s degree in Law, Public Policy, Computer Science, Cybersecurity, Data Science, Risk Management, or a related field.
Core Experience: 5+ years in governance, risk, compliance, cybersecurity, technology risk, or related functions.
Specialized Experience: 2+ years specifically focused on AI governance, responsible AI, model risk management, digital regulation, or AI compliance programs.
Technical Skills & Certifications
Governance Frameworks: Hands-on experience with AI governance and risk frameworks such as NIST AI RMF, ISO/IEC 42001, model risk management standards, and privacy-by-design approaches.
Regulatory Knowledge: Strong familiarity with global AI regulatory requirements, responsible AI principles, and audit/control evidence expectations.
Preferred Certifications: IAPP AIGP, CIPP, CIPM, CRISC, CISSP, ISO 42001-related credentials, or similar governance/risk certifications.
Soft Skills
Stakeholder Management: Strong executive communication and advisory skills with the ability to translate regulatory and governance requirements into practical operating models.
Policy Translation: Ability to bridge legal, compliance, technical, and business teams to operationalize AI governance in real-world delivery environments.
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