AI agentic engineer

Roles and Responsibilities :

Governance Architecture & Platform Engineering • Architect and implement an Agentic AI Governance layer, including an agent registry, tool/connector catalog, identity-scoped permissions, and real-time guardrails on LLM and tool-call outputs across student journey workflows. • Design and maintain an AI Use-Case Registry & Inventory, providing central intake, vendor AI inventory tracking across CRM/LMS/SIS, unique use-case IDs, lifecycle state tracking, and entity-level visibility. • Build a Risk Tier Classification Engine supporting automated and policy-driven multi-tier assignment, risk scoring, escalation triggers, and support for multi-tenancy and cross-domain risk axes. • Design and maintain Logging & Traceability infrastructure capturing prompts/hashes, model version, output type, override flags, and timestamps, ensuring a full audit trail for the multi-tier use cases. • Architect Kill-Switch & Rollback Controls, including documented per-use-case and per-vendor-feature disable paths, rollback documentation, kill-switch owner assignment, and auditability of disable events. • Evaluate and recommend the most appropriate AI governance, evaluation, and observability tools and frameworks, balancing scalability, integration, and maintenance considerations. Cross-Functional Collaboration & Multi-Entity Governance • Under the direction of the Director, design and implement Multi-Tenant / Multi-Entity Support, including entity-level data isolation, per-entity steward configuration, and cross-entity audit without data commingling. • Define and implement Human Oversight & Escalation Routing, including named oversight owner assignment, escalation workflows, and cross-entity routing logic determining which entity steward is triggered for which agent event. • Partner with counterparts across Infrastructure, InfoSec, Compliance, and Applications Engineering to align on security, governance, deployment, and performance standards. • Support the translation of high-level governance and compliance objectives into detailed technical designs, guiding implementation across teams. Delivery, Compliance & Operationalization • Own Deployment Governance & Gate Workflow processes, enforcing the 5-gate freeze rule, integrating with ADRs, and supporting approval routing, AI Control Authority sign-off, and exception management. • Ensure Compliance & Regulatory Alignment with FERPA, GLBA, SOC 2 Type II, and higher-ed sector frameworks, implementing policy-as-code for regulatory requirements and supporting automated compliance attestation. • Deliver Microsoft + Databricks Native Integration wherever possible – including Purview, Azure AI Foundry, Copilot Studio, Unity Catalog, and MLflow – favoring native connectors over custom middleware.

Job Requirements :

• 5-10 years of experience in IT services & consulting with expertise in AI engineering.
• Strong understanding of machine learning concepts, including supervised/unsupervised methods, deep learning techniques, etc.
• Experience working on large datasets to extract insights that drive business value.
• Familiarity with cloud-based platforms (e.g., Azure) for deploying AI models.