AI Governance & Responsible AI Consultant

Roles & Responsibilities :

This role defines the enterprise framework for safe, ethical, explainable, and compliant AI. Based in the AI CoE, the consultant sets Responsible AI policies, explainability and governance standards, AI risk classification, and regulatory readiness across the AI lifecycle — advising engineering, product, legal, and business teams, and orchestrating security testing. The mandate is to make AI trustworthy and auditable.

Key responsibilities

Responsible AI Framework

• Define and maintain Responsible AI principles, policies, and implementation guidelines — covering fairness, transparency, accountability, privacy, safety, robustness, and human oversight.
• Create Responsible AI assessment templates for AI and GenAI use cases.
• Define AI risk-classification approaches based on impact, data sensitivity, autonomy, user exposure, and regulatory relevance.
• Support review boards and governance forums for high-risk AI initiatives.

Explainability and Transparency

• Define explainability requirements by business criticality, risk level, and regulatory expectation — and advise teams on appropriate XAI approaches.
• Establish documentation standards — decision logs, datasheets.
• Support business teams in communicating AI-driven decisions in an accountable, and auditable manner.

AI Security and GenAI Risk – Advisory and orchestration

• Define AI security requirements and standards — model access, prompt security, data-leakage prevention, adversarial robustness, and abuse prevention — as policy and guardrails.
• Establish guidelines for GenAI-specific risks: prompt injection, jailbreaks, insecure tool use, data exfiltration, and unsafe outputs.
• Orchestrate AI security testing and red teaming; ensure findings are triaged, tracked, and remediated.
• Advise on AI operational risk, including LLMOps observability and cost/FinOps guardrails.

AI Governance and Regulatory compliance

• Develop AI lifecycle governance controls — risk assessment, design and data review, model validation, deployment approval, monitoring, and periodic reassessment.
• Support compliance with AI regulations and standards (e.g. EU AI Act, NIST AI RMF, ISO/IEC 42001) and sector-specific obligations.
• Collaborate with legal, compliance, data privacy, and cybersecurity teams to stay aligned with evolving expectations.

Consult for client engagements

• Provide expert guidance to project teams; participate in architecture, model-risk, and production-readiness reviews with practical, innovation-friendly guardrails.
• Support Service Line engagements with Responsible AI and governance consulting offerings — client assessments, workshops, and proposals.
• Develop and maintain governance playbooks, control libraries, and assessment frameworks.

Experience

• 8–12 years in AI governance, Responsible AI, model risk, data privacy, compliance, or AI/ML with a governance focus

Expected Skills

• Experience defining governance controls, risk frameworks, or compliance processes; ability to design assessment templates and control libraries
• Sufficient AI-security and red-teaming literacy to advise and orchestrate — not perform — security engineering
• Strong grasp of the AI/ML and GenAI lifecycle — LLMs, RAG, agents, deployment, and monitoring — and their risk surfaces
• Familiarity with EU AI Act, NIST AI RMF, ISO/IEC 42001 / 23894, GDPR, or sector-specific AI obligations
• Experience working alongside engineering and architecture teams in a services or product organization

Stakeholder skills

• Ability to translate complex AI risk into business-friendly guidance across technical, legal, risk, and business teams

Qualifications

Educational qualification:

B.E/B.Tech/MCA/PhD or equivalent Qualification

Experience :

• 8–12 years in AI governance, Responsible AI, model risk, data privacy, compliance, or AI/ML with a governance focus

Mandatory/requires Skills :

• Experience defining governance controls, risk frameworks, or compliance processes; ability to design assessment templates and control libraries
• Sufficient AI-security and red-teaming literacy to advise and orchestrate — not perform — security engineering
• Strong grasp of the AI/ML and GenAI lifecycle — LLMs, RAG, agents, deployment, and monitoring — and their risk surfaces
• Familiarity with EU AI Act, NIST AI RMF, ISO/IEC 42001 / 23894, GDPR, or sector-specific AI obligations
• Experience working alongside engineering and architecture teams in a services or product organization