AI Governance and Privacy Senior Consultant
- Designing pragmatic AI governance operating models (intake, risk tiering, approvals, documentation standards, exception handling, and audit readiness) with a focus on GenAI and agentic AI deployments.
- Building and maintaining AI system inventories (models, agents, tools, data sources, integrations), with clear ownership, intended use, risk classification, and change-control expectations.
- Conducting AI risk assessments for privacy, security, model risk, and misuse-including prompt injection, sensitive data exposure, excessive agency, and overreliance-and translating findings into implementable mitigations.
- Establishing technical control guidance for teams building agentic AI solutions: human-in-the-loop patterns, tool access controls, safe retrieval and grounding practices, logging/monitoring, token and data minimization, and incident response playbooks.
- Implementing "governance in the workflow" by integrating governance checkpoints into product and engineering delivery (architecture reviews, release gates, evaluation requirements, documentation automation, and evidence capture).
- Standing up or enhancing evaluation and monitoring approaches for GenAI systems: test plans, safety and quality metrics, red teaming workflows, and reporting dashboards for leaders and risk stakeholders.
- Partnering cross-functionally with Cybersecurity, Privacy, Legal, Risk, Engineering, and Data Science to drive adoption and ensure governance guidance is usable, measurable, and repeatable.
- Bachelor's degree or equivalent practical experience.
- 4+ years of experience in one or more of the following: AI governance, data privacy, security risk management, compliance and controls, AI product risk, model risk management, or technology risk consulting.
- Demonstrated experience translating policies and regulatory expectations into operational workflows, artifacts, and controls (e.g., intake processes, inventories, decision logs, risk registers, RACI, playbooks).
- Working knowledge of AI/ML/LLM systems and delivery lifecycles sufficient to assess real deployment risks and mitigations (training vs. RAG vs. fine-tuning vs. tool use, data dependencies, integration patterns).
- Software development fluency: ability to collaborate with engineering teams on implementation details; ability to prototype or automate governance workflows in Python/SQL and to understand CI/CD and cloud deployment basics.
- Practical experience with privacy program execution and artifacts (PIAs/DPIAs, vendor reviews, data inventories, data minimization, retention, and access control principles).
- Ability to communicate clearly with both technical and non-technical stakeholders and produce executive-ready reporting.
- Ability to travel 0-50%, on average, based on client and project needs.
- Limited immigration sponsorship may be available.
- Previous consulting or Big 4 experience.
- Hands-on experience operationalizing AI governance aligned to frameworks such as the NIST AI RMF and/or ISO/IEC 42001, with awareness of risk-based AI regulatory regimes (e.g., EU AI Act).
- Experience with GenAI safety and evaluation practices (prompt injection testing, jailbreak resilience, hallucination measurement, toxicity/harm scoring, grounding effectiveness).
- Familiarity with governance tooling and workflow platforms (e.g., OneTrust, GRC platforms, ticketing/workflow systems) and how to integrate them into engineering delivery.
- Certifications such as CIPP/US, CIPM, IAPP AIGP, CISM, or CISSP.
- Prior experience in cyber or enterprise security contexts (data security, identity, audit logging, secure SDLC).
- Experience designing Human-in-the-Loop escalation pathways, exception handling, and automated safety protocols for highly autonomous systems.
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