Head of Agentic AI Platform Engineering

Job Description 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.  Qualifications Bachelor’s or Master’s Degree or equivalent.  Plus, broad 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 here persuades: domain experts to engage, stewards to share what they know, sponsors to stay honest about value, and functions like Legal, Quality, and Security to move from gatekeeping to partnership. We look for people who write and speak clearly, who adapt their register from bench scientist to Board, and who change minds through credibility and clarity rather than escalation.   Hands-on experience building production agent systems or LLM orchestration infrastructure at enterprise scale; deep familiarity with MCP, A2A, and the emerging interoperability landscape.   Strong identity and security instincts; experience integrating with enterprise IdP and entitlement systems.   Experience designing for regulated environments, ideally including computerized system validation or Part 11 contexts.   A conviction that evaluation is an engineering discipline, and the scars to prove it.   Additional Details This job has a full time weekly schedule. Our pay ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. During the hiring process, a recruiter can share more about the specific pay range for a preferred location. Pay and benefit information by country are available at: https://careers.agilent.com/locations Agilent Technologies Inc. is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other protected categories under all applicable laws. Travel Required: 10% of the Time Shift: Day Duration: No End Date Job Function: R&D