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

Goldman Sachs•Dallas, TX

About The Position

The Vice President will join the Operational Risk Department’s AI Coverage function, which strengthens oversight of operational risks arising from the firm’s adoption and use of artificial intelligence. The role continuously identifies, monitors, measures, and assesses risks associated with AI platforms, foundation models, agentic systems, and their supporting infrastructure, vendors, and data. It helps ensure the firm’s AI footprint is built, operated, and governed consistently with operational resilience, third-party risk, and data governance frameworks, policies, and standards. Candidates may bring deep expertise in one or more adjacent risk disciplines, provided they have direct experience evaluating or managing risk in AI, machine learning, or agentic technology environments. The position covers AI resilience and operational continuity, AI third-party and supply-chain risk, AI data governance, and cross-cutting operational risk management. It includes assessing AI-dependent services and systems, challenging controls, coordinating scenario analysis and testing, overseeing vendor and data-related risks, analyzing risk indicators, supporting incident reporting and risk assessments, and driving improvements to AI risk practices. The role also requires keeping current with relevant regulatory and industry developments.

Requirements

At least 7 years of relevant experience in operational risk, resilience/BCP, third-party risk management, data governance, a financial institution’s technology division, or a technology company building or maintaining enterprise AI/ML systems.
Direct experience applying one or more of operational resilience, third-party risk management, or data governance disciplines to AI/ML systems.
Strong understanding of AI/ML architecture, including foundation models, LLMs, RAG systems, agentic AI frameworks, MCP servers, vector databases, and model deployment infrastructure.
Experience with resilience-by-design, AI vendor or supply-chain controls, or data quality control frameworks applied to AI pipelines.
Knowledge of relevant control frameworks such as NIST 800-53, NIST AI 600-1, ISO 27001, EU AI Act, and/or the firm’s Data Governance Policy/Standard.
Strong verbal and written communication skills, including the ability to present with impact and influence.
Ability to work in a fast-paced environment with a strong delivery focus and strong organizational skills.
Bachelor’s degree in Computer Science, Cybersecurity, Business and Technology Management, Finance, Data Science, or a related discipline.

Nice To Haves

Project management experience.
Proficiency in SQL, graph databases, and Tableau.
Relevant certifications such as CISA, CISM, CBCP/MBCI, CTPRP, or related AI/ML, data, and resilience certifications.

Responsibilities

Identify, monitor, and analyze operational risks related to AI system resilience, including infrastructure or model outages, behavior under stress, multi-agent failures, model degradation, and unavailability of third-party services.
Ensure AI systems that are dependent or integral assets to Important Business Services are mapped in Business Continuity Plans, covered by Impact Tolerance Statements, and supported by alternate workarounds such as model fallbacks or alternate vendors.
Promote resilience-by-design principles and challenge gaps in controls such as model version pinning, system observability, multi-data-center deployment, and circuit-breaker or fallback procedures.
Conduct evidence-based scenario analysis and support integrated or asset-based testing of AI-dependent services, coordinating with engineering, third-party risk, and business stakeholders to assess whether impact tolerances are met during disruption.
Identify, monitor, and analyze risks from third-party AI services, externally hosted LLMs, and vendor-supplied foundation models, including information leakage, model tampering, supply-chain compromise, and agent-mediated credential harvesting.
Oversee vendor due diligence, third-party model validation, and SLA enforcement for AI safety and performance, including Denial-of-Wallet monitoring; escalate contractual breaches.
Monitor the sufficiency and completeness of the AI third-party control inventory, including controls for data leakage prevention, MCP server security governance, and agentic-system credential protection.
Apply firmwide data governance policies and standards to AI use cases, ensuring datasets used to train, fine-tune, ground, or operate AI systems are inventoried, risk-tiered, and assigned Owners, Secondary Owners, and Senior Owners.
Challenge data quality controls and data lineage documentation for datasets in AI model pipelines, particularly when data crosses divisional boundaries or lacks Data Contracts.
Assess whether authoritative data sources used by AI/ML systems meet governed-access, reconciliation, and change-management requirements; escalate reliance on non-authoritative or ungoverned sources as data defects or exceptions.
Review AI-related Data Defects and Data Requests for appropriate logging, remediation, and escalation.
Analyze trends in AI availability, observability telemetry, vendor performance, and data quality metrics; escalate concerns to senior management when warranted.
Facilitate operational risk event reporting for AI resilience, third-party, and data-related incidents.
Contribute to divisional risk profile assessments, RCSA review and challenge, and New Activity reviews for AI model deployments, vendor engagements, and data sourcing changes.
Remain current on regulatory and industry developments affecting AI resilience, third-party risk, and data governance obligations.
Identify and drive initiatives to improve AI risk management practices across resilience, third-party, and data domains.