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
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
