Head of Agentic AI Platform Engineering

Owns the Fabric agent plane: the runtimes and integration spine that turn data into agents that act. The architectural commitment is vendor neutrality by construction: orchestration runs on open MCP and A2A standards, experience surfaces are rented, and the intelligence stays in the Fabric. Swapping a frontier model provider must be a mechanical change at the model gateway, never a re-architecture.   This role also carries the engineering side of the two-mode access model. Builders author in sandboxes and promote through eval gates; Consumers compose certified assets in self-service. The harness makes that ladder safe, because composition inherits the risk tier of whatever it touches, and the harness enforces that inheritance.   Responsible for   Agent runtimes, orchestration, and the model gateway, with the discipline of minimal, distinct, non-overlapping agent skills that are independently testable.   The MCP / A2A integration spine across every layer of the Fabric: one protocol, every layer.   Reference patterns and the shared   eval   harness,   operated   jointly with Trust, Risk & Evaluation.   The agent identity and entitlement implementation in partnership with IT and Security, covering user identity, agent identity, and entitlement checks at the data product boundary, with full lineage and audit.    The   GxP   commit-point pattern   and support for audit, traceability   Solid-line management of AI Harness Engineers deployed into pods.   Leads the development and execution of enterprise-wide technology architecture strategies, ensuring alignment with business and IT objectives. Oversees teams of Enterprise, Solutions, and specialized architects to design and implement integrated architectures across applications, cloud, data, infrastructure, and security. Responsibilities include driving target architecture realization, conducting cost-benefit and risk analyses, monitoring compliance with standards, and advocating continuous improvement to maximize efficiency. Evaluates emerging technologies, guide technical decisions, and ensure scalable, cost-effective solutions that support organizational goals. What success looks like in year one   Production agentic workflows live across the   first-wave   use cases, each composing registered skills with documented risk tiers and human-in-the-loop policy.   The MCP spine   adopted   as the default integration path for new AI work.   Agent identity propagation working end to end for at least one regulated and one non-regulated use case, with full audit trail.   Promotion through eval gates   operating   as routine practice, with sandbox-to-certified cycle time measured and improving.

Bachelor’s or Master’s   Degree or equivalent .     Plus, b road knowledge of functional area(s) of responsibility.   Minimum of 10 years' experience formally or informally leading people,   projects   and/or programs.   Curiosity about AI, its potential and its pitfalls. The field moves monthly, and the people who thrive here are genuinely curious about both sides of it: what these systems can newly do, and where they fail, mislead, or quietly degrade. We want people who read the failure analyses as eagerly as the launch posts, who experiment on their own initiative, and who hold excitement and skepticism at the same time without letting either one win permanently.    Lifelong learners. Whatever expertise  a candidate arrives with will be partially obsolete within a year, and that is not a defect of the candidate; it is the condition of the field. We hire people who have reinvented their toolkit before and expect to do it again, who learn in public, and who treat being wrong as information rather than injury. A history of deliberate self-reinvention counts for more than any single credential.    Excellent communication and the ability to influence. Nothing in this organization ships by authority alone. Every role