AI Engineer – Controls & Capability Enablement

About Rockefeller Capital Management

Rockefeller Capital Management was established in 2018 as a leading independent financial advisory services firm. Originally founded in 1882 as the family office of John

D. Rockefeller, the Firm has evolved to offer strategic advice to ultra- and high-net-worth individuals and families, institutions, and corporations from offices in 35 markets throughout the United States, as well as an office in London. The Firm oversees $228 billion in client assets as of July 31, 2026.

The primary purpose of this role is to enable the safe, deliberate, and accelerated adoption of AI across Rockefeller Capital Management. Partnering closely with Risk, Compliance, Cybersecurity (CISO), and AI Risk Governance teams, the individual translates governance requirements into scalable technical controls that govern the firm's AI ecosystem, including agents, copilots, skills, plugins, connectors, and the large language models that power them.

AI platforms evolve rapidly, with new models, capabilities, and features released on a near-weekly basis. This role is responsible for staying ahead of that pace by continuously evaluating emerging technologies from providers such as Microsoft, Anthropic, and other vendors. The individual will assess new capabilities through both a business value and risk lens, providing clear recommendations on whether they should be adopted, piloted, restricted, or deferred.

This is a highly hands-on technical role focused on operationalizing AI governance. Rather than creating policy, the individual interprets existing requirements and implements them directly through platform configuration, policy enforcement, APIs, automation, and administrative tooling across Microsoft and Anthropic environments. The goal is to ensure AI controls are embedded, measurable, and enforceable while supporting the firm's continued innovation and growth.

• Interpret and implement enterprise control requirements. Translate policies, standards, and risk decisions issued by Risk, Compliance, the CISO organization, and AI Risk Governance into concrete technical controls, review gates, and deployment standards for AI capabilities.
• Configure and script the controls. Implement guardrails directly across Microsoft and Anthropic services — tenant and admin center settings, Copilot and Copilot Studio policies, Azure AI Foundry configuration, Anthropic enterprise administration, Entra access policies, and Purview data protection controls.
• Automate governance ld scripts and automation (Power Shell, Graph API, Python, KQL) to inventory agents and skills, enforce policy, detect drift, monitor usage, and produce audit and compliance evidence.
• Own agents and skills end to end. Maintain the inventory, ownership model, and lifecycle — from intake and review through deployment, monitoring recertification, and retirement.
• Run capability review assessments. Evaluate new AI features and use cases against firm-defined risk, security, and compliance criteria — covering business value, security exposure, data access and entitlement impact, supervisory obligations, operational readiness, and fit with enterprise
• architecture standards — and elevate to the appropriate governance body where a policy decision is required.
• Assess LLMs and model changes. Test and compare models across providers, track model and version updates, evaluate capability shifts and failure modes, and provide the technical evidence governance bodies need to decide where each model may be used.
• Stay ahead of AI evolution. Systematically track roadmaps, release notes, and previews across Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry, Anthropic, and emerging platforms, and translate each change into a…