Senior Manager - Responsible AI Operations

ANSR is hiring for one of its clients.

Job Title: Senior Manager - Responsible AI Operations (Responsible AI & AI Governance)

About ANSR MedTech:

Who We Are:

ANSR MedTech Capability Center is a new global innovation hub being established in India for a Fortune 100 Fastest-Growing Company in the MedTech sector. Built in partnership with ANSR, the center draws on ANSR's proven experience in establishing and scaling high-performance Global Capability Centers (GCCs) for leading global enterprises.

ANSR MedTech center brings together world-class engineering, product, and technology talent to build next-generation healthcare platforms and solutions that power global operations.

Job Summary:

The Responsible AI Operations will own and operate the end-to-end Responsible AI governance program for the India COE, accountable for the AI registry and use-case intake, model risk assessment and tiering, accountability report reviews, policy and standards development, and the enterprise Responsible AI training and enablement curriculum.

This role reports to the Director of Applied AI with a dotted line to Head of Enterprise Data and AI and operates within a multi-disciplinary organization, partnering closely with legal, privacy, security, quality, regulatory affairs, data governance, data science, and AI engineering teams to ensure every AI and generative-AI system is developed, reviewed, deployed, and monitored to the standards required in a regulated MedTech environment.

Key Responsibilities:

Responsible AI Governance Process & Operating Model:

• Own and operate the Enterprise Responsible AI governance process end-to-end, from use-case intake and triage through risk assessment, review, approval, deployment, and post-deployment monitoring.
• Define and maintain the Responsible AI policy, standards, and control framework covering fairness, transparency, explain ability, safety, privacy, security, human oversight, and accountability
• Establish and chair the Responsible AI governance forums, preparing materials, driving decisions, documenting outcomes, and tracking conditions of approval to closure
• Define risk tiering criteria for AI and generative-AI use cases, and calibrate depth of review, required evidence, and approval authority to the assessed risk level
• Establish stage-gate criteria and definition-of-done for Responsible AI compliance, and ensure no AI system reaches production without documented review and sign-off
• Maintain a clear RACI across product, engineering, data governance, legal, privacy, security, quality, and regulatory stakeholders.

AI Registry & Inventory Management:

• Own the enterprise AI registry as the authoritative inventory of AI, ML, and generative-AI systems, including ownership, purpose, data sources, model lineage, risk tier, lifecycle status, and approval history
• Define registry data standards, metadata requirements, and intake workflows, and drive completeness, accuracy, and timely updates across business and technology teams
• Operate registry lifecycle management, including onboarding of new use cases, periodic recertification, change tracking for material model or scope changes, and retirement of decommissioned systems
• Partner with platform and engineering teams to automate registry capture from MLOps and LLMOps pipelines, model catalogs, and deployment tooling
• Produce registry-driven reporting on AI portfolio composition, risk exposure, review status, and audit readiness for senior leadership.

Accountability Reporting, Assessments & Audit:

• Own the accountability report process for AI systems: define templates, evidence requirements, and review cadence, and lead reviews of submitted documentation for completeness and quality
• Lead model risk, impact, and bias assessments in partnership with data science teams, including evaluation of training data, fairness metrics, performance across subpopulations, and failure modes
• Review evaluation results, guardrail coverage, human-in-the-loop design, and monitoring plans, and challenge submissions where evidence does not support the claimed risk posture
• Maintain the audit trail and documentation set required to demonstrate compliance to internal audit, quality, regulatory bodies, and external assessors
• Coordinate responses to internal and external audits, regulatory inquiries, and customer or partner assurance requests relating to AI systems
• Define, track, and report Responsible AI KPIs and control effectiveness metrics, including review cycle time, registry coverage, issue aging, and remediation closure rates.

Regulatory Alignment & Data Governance Partnership:

• Translate evolving AI regulation and standards (e.g., EU AI Act, NIST AI Risk Management Framework, ISO/IEC 42001) and applicable MedTech quality and regulatory requirements into practical internal controls
• Partner with data governance to align AI controls with data quality, master data, lineage, classification, retention, and access-management standards
• Partner with privacy and security to define guardrails for approved models, sensitive and regulated data usage, third-party and vendor AI tools, and human oversight requirements
• Assess and govern third-party and vendor-supplied AI capabilities to the same standards applied to internally built systems
• Monitor the external regulatory landscape and proactively update policies, standards, and assessment criteria as requirements evolve.

Training, Enablement & Culture:

• Design and deliver the enterprise Responsible AI training curriculum, tailored by audience: executives, product owners, engineers, data scientists, and business users
• Build practitioner-facing playbooks, checklists, templates, and self-service guidance that make the governance process fast, clear, and repeatable
• Run enablement campaigns, office hours, and communities of practice to embed Responsible AI practices into day-to-day delivery rather than as a late-stage gate
• Track training completion, competency, and adoption, and report coverage to leadership and compliance stakeholders.

Leadership & Stakeholder Management:

• Build and lead a team of Responsible AI, governance, and risk professionals, including hiring, coaching, and performance development
• Manage senior stakeholders across business, technology, legal, quality, and regulatory leadership, communicating risk posture, issues, and remediation progress
• Act as escalation point for contested risk decisions, balancing innovation velocity against regulatory and reputational risk
• Contribute to the COE shared library of governance patterns, reusable controls, and assessment artifacts
• Participate in roadmap definition, capacity planning, and cross-functional reviews within the I&A organization.

Qualifications:

• Bachelor's degree or above in computer science, data science, statistics, engineering, information management, law, or a related field
• 15+ years of experience across data governance, data science, AI/ML, risk, compliance, or technology assurance, with significant time in a regulated industry
• Proven experience establishing and operating an AI governance, Responsible AI, or model risk management program at enterprise scale
• Hands-on experience owning an AI or model inventory/registry, including intake, risk tiering, recertification, and lifecycle management
• Strong working knowledge of AI/ML and generative-AI concepts: model development lifecycle, evaluation, RAG and agentic patterns, guardrails, and monitoring, sufficient to credibly challenge technical submissions
• Practical knowledge of AI regulation and standards such as the EU AI Act, NIST AI RMF, and ISO/IEC 42001, and how they translate into operational controls
• Experience with data governance disciplines: data quality, lineage, classification, privacy, retention, and access management
• Demonstrated success designing and delivering training, enablement, and change programs for technical and non-technical audiences
• Experience leading audits, assessments, or regulatory inspections and producing defensible documentation and evidence
• Demonstrated success building and leading teams, including hiring, mentoring, and vendor management
• Excellent communication, facilitation, and influencing skills, with the ability to align legal, technical, and business stakeholders on pragmatic outcomes.

Preferred Skills:

• Experience in MedTech, life sciences, healthcare, pharma, or another highly regulated industry, including familiarity with quality management systems and computer software assurance
• Familiarity with GRC platforms and Responsible AI tooling for assessments, documentation, and control tracking
• Experience with Databricks (Unity Catalog), Microsoft Purview, or equivalent catalog and governance tooling
• Hands-on exposure to model evaluation, bias and fairness testing, and content-safety or guardrail tooling (e.g., Azure AI Content Safety)
• Relevant certifications in privacy, risk, audit, or AI governance (e.g., CIPP, CRISC, CISA, ISO/IEC 42001 lead auditor).