- Own the technical configuration, administration, and operational management of the Funds' enterprise AI and agentic infrastructure, including platform settings, environments, integrations, access controls, logging, and monitoring.
| - Configure and manage agentic AI platforms and operating environments, including an AI-driven Software Development Lifecycle (SDLC) automation platform, ensuring that settings, permissions, guardrails, connectors, and deployment standards are implemented consistently and securely.
| - Evaluate AI coding and agent tools such as Claude, GitHub Copilot, OpenAI/Codex, and Databricks Genie for use by the organization to address business needs.
| - Deploy, configure, and maintain AI models, agents, skills, prompts, workflows, and related services across approved enterprise platforms such as Microsoft 365 Copilot, Azure OpenAI, Azure Databricks (Omnigent, Genie), Microsoft Foundry, Claude, and other sanctioned AI tooling.
| - Manage MCP servers, agent-to-system integrations, API connections, knowledge sources, tool access, and other technical components required to enable secure agentic workflows.
| - Implement technical guardrails, including role-based access, data-loss-prevention controls, prompt and tool restrictions, model-use boundaries, approval workflows, audit logging, and monitoring alerts.
| - Establish usage, performance, reliability, and cost-management practices for AI infrastructure, including token consumption, licensing, compute utilization, environment sprawl, and vendor/platform cost controls.
| - Collaborate with Infrastructure, Development, Security, and Data teams to ensure AI services are deployed within approved technical architectures and integrated appropriately with existing enterprise systems.
| - Translate AI governance requirements into enforceable technical controls, including platform configuration standards, approved deployment patterns, restricted-use settings, and automated or manual approval checkpoints.
| - Develop and maintain enterprise AI technical standards and reference architectures; participate in AI architecture reviews and the AI Center of Excellence; and contribute to the enterprise AI technology roadmap.
| - Maintain the technical AI inventory, and lifecycle of deployed models, agents, prompts, skills, MCP servers, connectors, knowledge sources, integrations, and environments, including owners, access levels, versioning, monitoring, change management, retirement, and cost profiles.
| - Conduct or support pre-deployment technical risk reviews focused on PHI/ePHI exposure, data residency, storage and retention, system integration risk, model/tool access, logging, auditability, and controls against prompt injection, data leakage, and unauthorized tool use.
| - Operationalize the Funds' AI Tool Vetting and Approval process, administering the intake queue, applying the risk-tiered classification model (Low / Medium / High / Prohibited), and routing requests for appropriate review. Low request items will be deployed without further review.
| - Partner with Information Security, the vCISO, and Compliance to ensure AI infrastructure aligns with cybersecurity standards, incident-response processes, HIPAA/HITECH requirements, and the Funds' internal control environment.
| - Support evaluation of third-party AI platforms and infrastructure components, including technical security requirements, data-use terms, model training restrictions, retention practices, logging capabilities, breach-notification obligations, and integration risks.
| - Monitor deployed AI services for unauthorized data exposure, policy violations, cost anomalies, model or agent misuse, integration failures, and drift from approved technical configurations.
| - Document control decisions, configuration standards, mitigation requirements, exceptions, and remediation plans in coordination with governance, security, and infrastructure leadership.
| - Develop and maintain technical standards, runbooks, deployment procedures, configuration checklists, and operating practices for AI platforms, agents, models, skills, MCP servers, and related services.
| - Produce operational dashboards and reports covering usage, licensing, infrastructure cost, token or compute consumption, platform utilization, deployed agents and skills, open risks, exceptions, and remediation status.
| - Serve as the technical implementation lead for AI infrastructure initiatives, ensuring the Funds has the platform capacity, controls, and deployment mechanisms needed to support a growing portfolio of AI projects.
| - Partner with business-facing teams as needed to understand implementation requirements, while keeping primary accountability for the technical environment, deployment architecture, and operational controls.
| - Partner with Enterprise Architecture to ensure AI platforms, services, and integrations align with enterprise technology standards and architecture.
| - Participate in AI governance discussions by providing technical feasibility assessments, infrastructure readiness updates, cost implications, control recommendations, and implementation constraints.
| - Stay current on emerging AI platform architectures, agentic infrastructure patterns, MCP standards, AI security controls, and responsible deployment practices, ensuring the Funds' technical environment remains secure and scalable.
| - Perform other duties as assigned by management.
| - Provide support outside of regular working hours and on weekends.
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