Manager, AI Platform & Agentic Orchestration

BLDG SVC 32 B-J•New York, NY

4d•$175,000 - $200,000

About The Position

The Manager, AI Platform & Agentic Orchestration is a hands-on technical leadership role responsible for building, configuring, operating, and governing the infrastructure that enables enterprise AI and agentic capabilities across Building Service 32BJ Benefit Funds (the "Funds"). The position focuses on the technical foundation required to safely deploy AI platforms, agents, models, skills, integrations, guardrails, and related services at scale. Rather than serving primarily as a business-facing solutioning role, this position owns the operational architecture and day-to-day management of the Funds' AI platform environment, including agentic infrastructure, model deployment, skill deployment, MCP server management, configuration standards, access controls, monitoring, usage management, and cost controls. The Manager, AI Platform & Agentic Orchestration works closely with IT Infrastructure, Security, Development, Data Strategy, Data Governance, and Compliance to ensure that AI capabilities can be implemented securely, reliably, and cost-effectively while protecting participant data and maintaining alignment with HIPAA, HITECH, DOL cybersecurity guidance, and internal policies. The role is expected to collaborate with technical and security leaders, including security counterparts such as the Information Security team, to ensure appropriate controls, guardrails, and incident-response practices are built into the AI operating environment.

Requirements

5+ years of experience in IT infrastructure, cloud engineering, platform administration, systems integration, DevOps, cybersecurity, data platforms, or enterprise technology operations.
Hands-on experience deploying, configuring, operating, or securing enterprise AI, automation, cloud, integration, or data platforms is strongly preferred.
Experience with vendor/platform management, technical security reviews, operational monitoring, cost management, and production support practices.
Healthcare, benefits administration, HIPAA-regulated, or highly regulated enterprise experience preferred.
Strong hands-on technical knowledge of enterprise AI platforms, generative AI services, agentic architectures, model deployment patterns, skill deployment, API integrations, and secure platform operations.
Working knowledge of AI/ML concepts and AI-specific risks, including prompt injection, data leakage, unauthorized tool use, model drift, hallucination risk, explainability, and output reliability.
Experience configuring and operating AI platforms, cloud-based AI services, Microsoft 365 Copilot, Azure OpenAI, MCP servers, agent frameworks, or comparable enterprise AI infrastructure.
Working knowledge of prompt engineering and versioning, model and prompt lifecycle management, agent orchestration, MCP architecture, RAG and vector technologies, AI evaluation and observability, Git/version control, APIs, Python, Azure AI Foundry/Azure OpenAI, and AI governance and guardrail technologies.
Strong understanding of cybersecurity, access controls, logging, monitoring, incident response, data-loss prevention, and secure integration practices.
Understanding of data privacy and regulatory compliance requirements, particularly HIPAA and HITECH, as well as familiarity with DOL cybersecurity guidance and emerging AI governance expectations.
Ability to manage AI infrastructure costs, licensing, token/compute consumption, utilization reporting, vendor/platform usage, and environment governance.
Demonstrated ability to create technical standards, runbooks, configuration documentation, operating procedures, and deployment controls.
Strong collaboration skills with technical infrastructure, development, security, compliance, privacy, and data governance stakeholders.
Clear communication skills, with the ability to explain technical risks, controls, costs, and implementation requirements to both technical and non-technical stakeholders.
AI platform administration and agentic infrastructure operations.
Model, agent, skill, and MCP server deployment.
Technical guardrails, access controls, and secure configuration management.
AI infrastructure monitoring, usage reporting, and cost management.
Cybersecurity, privacy, and compliance alignment, including HIPAA/HITECH.
Technical documentation, runbooks, deployment standards, and operational discipline.
Vendor/platform evaluation and third-party technical risk review.
Collaboration with infrastructure, development, security, compliance, and data governance teams.
Problem-solving, systems thinking, and implementation-focused execution.
Detail oriented with excellent organization and analytical skills.
Ability to plan, take initiatives to accomplish objectives in a timely fashion, and work independently.
Ability to prioritize work and meet deadlines.
Ability to establish and maintain effective working relationships with project team members, supervisors, and other employees.

Nice To Haves

Relevant certifications or a Master's degree preferred.
Certifications such as Microsoft Azure Administrator, Azure AI Engineer, Azure Security Engineer, CISSP, CISM, CRISC, cloud architecture, DevOps, cybersecurity, or AI governance/responsible-AI credentials are beneficial.
Project-management certification is a plus but not required.

Responsibilities

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.