Risk, Technology Operational Risk – CORE Engineering Risk, Vice President, Dallas Dallas Vice President

Risk, Technology Operational Risk – CORE Engineering Risk, Vice President, Dallas  Dallas, TX, United States

The Operational Risk Department at Goldman Sachs is an independent risk management function responsible for developing and implementing a standardized framework to identify, measure, and monitor operational risk across the firm. AI Coverage is a specialized role within this framework, dedicated to strengthening the firm's oversight of operational risks arising from the firm's adoption and use of artificial intelligence. This professional will be responsible for continuously identifying, monitoring, measuring, and assessing operational risks associated with AI platforms, foundational models, agentic systems, and the infrastructure, vendors, and data that support them — ensuring the firm's AI footprint is built, operated, and governed in a manner consistent with the firm's operational resilience, third-party risk, and data governance frameworks, policies, and standards.

The role is intentionally broad: the successful candidate may bring deep expertise in one of several adjacent risk disciplines, provided that expertise is grounded in direct experience evaluating or managing risk in AI, machine learning, or agentic technology environments.

• Identify, monitor, and analyze operational risks arising from AI system resilience, including infrastructure unavailability, foundational model outages, non-deterministic behavior under stress, cascading multi-agent failures, model degradation from data drift, and unavailability of third-party hosted LLMs and MCP server ecosystems.
• Ensure AI systems qualifying as dependent or Integral Assets to Important Business Services (IBSs) are mapped within Business Continuity Plans, subject to Impact Tolerance Statements, and supported by alternate workaround strategies (e.g., model fallback, alternate vendors).
• Champion resilience-by-design principles (modularity, diversity, redundancy, adaptability) and challenge gaps in controls such as AI Model Version Pinning, AI System Observability, multi-data-center deployment, and circuit-breaker/fallback procedures.

Conduct evidence-based scenario analysis and support integrated/asset-based testing of AI-dependent IBSs, coordinating with 1

LoD Engineering, TPRM, and business stakeholders to confirm the firm remains within impact tolerances (RTO/RPO/SLA) during disruption.

• Identify, monitor, and analyze operational risks from third-party AI services, externally hosted LLMs, and vendor-supplied foundation models, including information leakage, model tampering, MCP server supply chain compromise, and agent-mediated credential harvesting.
• Oversee vendor due diligence, third-party model validation, and SLA enforcement for AI safety, performance, and Denial-of-Wallet monitoring, escalating contractual breaches.

Monitor the AI third-party control inventory (e.g., AI Data Leakage Prevention, MCP Server Security Governance, Agentic System Credential Protection Framework) for sufficiency and completeness.

• Apply the Firmwide Policy and Standard for Data Governance to AI use cases, ensuring Datasets used to train, fine-tune, ground (e.g., RAG/vector databases), or operate AI and agentic systems are inventoried, risk-tiered, and have clearly assigned Owners, Secondary Owners, and Senior Owners.
• Challenge the adequacy of Data Quality controls (accuracy, completeness, timeliness) and Data Lineage documentation for datasets feeding AI model pipelines, particularly where data crosses divisional boundaries or lacks Data Contracts.
• Assess whether Authoritative Data Sources (ADS) used by AI/ML systems meet governed-access,…