Director, AI Architecture & Agent Platform

Job Description

Position Summary

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TKO Group Holdings, the parent of UFC, WWE, IMG, On Location and PBR, is deploying AI across multiple business units and a growing number of platforms.

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The Director, AI Architecture & Agent Platform sets TKO's technical architecture for agents and AI platforms. This role determines how agents are designed, connected, evaluated, secured, observed and operated across TKO. It owns the technical point of view on orchestration, reusability, interoperability and platform choice, including where TKO should build, buy, use a platform-native capability or use an independent layer.

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The role validates the architecture through targeted reference builds, including the first cross-system agent taken to production, then enables engineering and business-unit teams to deliver against reusable standards. It is not a central application delivery role or a high-volume build service.

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This is an architecture-led individual-contributor role. It requires enough hands-on depth to prototype critical paths and challenge technical claims, along with the judgment and influence to guide work through teams that do not report to this role.

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Responsibilities

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1. Agentic Harness and Orchestration Architecture

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• Own TKO's technical point of view on where orchestration and reusability should sit, including build versus buy and platform-native versus independent options.
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• Define what each option requires, what it forecloses, and how agents built on different platforms will interoperate.
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• Lead the TKO-side answer to the initial orchestration assessment, working with an external partner while challenging the analysis and making the recommendation.
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• Define portability and migration criteria for platform-specific agents, including how TKO separates business logic, prompts, tools, identity, data access and evaluation from any one platform. Establish a path to rehost or retire agents built on IBM Watson, Salesforce or other platforms when warranted.
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• Establish the architecture principles and decision records that keep the agent platform coherent as the estate grows.
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2. Reference Architecture and Technical Validation

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• Lead the architecture and technical direction for the first cross-system agent built through the new standard and taken to production.
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• Personally prototype or implement the critical parts needed to validate the architecture, then partner with delivery teams on the broader build.
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• Produce the templates, reusable components and engineering patterns that later builds will use.
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• Make the first-of-a-kind patterns real without becoming the long-term application owner or central delivery team for every use case.
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3. Technical Evaluation, Estimation and Portfolio Input

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• Assess AI tooling and proposed solutions on technical merit at a level that can withstand challenge from engineers and vendors.
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• Provide architecture, feasibility, effort, total-cost and technical-risk input into portfolio prioritization.
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• Write technical assessments that make vendor claims, dependencies, limitations and tradeoffs explicit.
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• Identify when a capability should be adopted, piloted, built, deferred or retired.
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4. AI Engineering Standards, Guardrails and Sandboxes

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• Define what must be true before an agent reaches production, including identity and access, evaluation and regression testing, observability, cost controls and failure handling.
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• Define standards for agents that take consequential actions, including payments or writes to source systems: authorization, human approval, segregation of duties, transaction limits, audit trails, idempotency, rollback and failure handling, data protection and testing. Own the AI architecture standard while platform, payment, security and business owners retain responsibility for their underlying controls.
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• Design and operate controlled sandboxes for emerging models and tools, including video-gen models such as Seedance. Give teams a safe way to test approved use cases without exposing TKO data, talent likeness, intellectual property or production systems.
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• Advise Engineering teams adopting tools such as Claude Code on repository and data access, secrets, code review, testing, dependency and intellectual-property controls, logging, cost limits and approved use.
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• Serve as the technical counterpart to Cybersecurity and other functions adopting AI tools. Help assess proposed uses, define safe operating practices, capture reusable patterns and align local decisions with TKO's enterprise architecture and guardrails.
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• Set the access controls, logging, spend limits, data and content rules, evaluation requirements and path to production or shutdown for those sandboxes.
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• Maintain an architecture decision and exception process: document the standard, approve deviations with named owners and expiry dates, and escalate unresolved cross-functional decisions with a recommendation.
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• Define the production lifecycle for agents and AI tools, including incident response, provider outages, model and version changes, material regressions and retirement, with clear handoffs to IT, Cybersecurity and business owners.
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Role Boundaries

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• This role owns TKO's AI architecture, technical standards, reference patterns and architecture decisions.
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• Platform owners and Engineering own implementation and operation of their platforms. IT owns infrastructure, provisioning and service delivery.
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• Cybersecurity, Legal, Finance and Privacy retain ownership of their underlying controls and approvals. This role provides the AI-specific architecture and technical standard they apply.
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• Business units own local use cases, adoption and business outcomes. This role helps them use common patterns and decides when a local solution should become an enterprise pattern.
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Required Qualifications

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• Experience architecting and operating agentic systems in production, not just prototypes, with a clear understanding of what failed, why it failed and how it was fixed.
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• Hands-on experience with orchestration frameworks such as LangGraph or LangChain, Model Context Protocol (MCP), tool-calling patterns and retrieval architectures.
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• Experience designing identity and access for non-human actors, including service accounts, delegated identity and role-based agent identity.
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• Experience evaluating non-deterministic systems, including test-set design, regression testing, quality gates and launch-readiness decisions.
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• Experience designing observability and cost controls for token-priced environments.
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• Experience designing controlled sandboxes or evaluation environments for emerging AI tools, with access controls, logging, data and intellectual-property restrictions and spend limits.
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• Security architecture literacy sufficient to be a credible counterpart to a Chief Information Security Officer (CISO) organisation.
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• Ability to write a technical assessment that engineers and vendors cannot bluff past.
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• Experience setting standards for AI systems that take actions in enterprise systems, including authorization, approval, auditability and failure recovery.
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• Experience advising technical teams on the secure adoption of AI developer tools and specialist AI platforms.
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• Ability to act as the senior technical voice in the room, prototype critical paths when needed and deliver through engineering and business-unit teams.
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Not Required

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• Model training or fine-tuning depth
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• Data platform or pipeline engineering experience
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• Owning ongoing feature delivery for business applications
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• Experience managing a large engineering organisation
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Preferred Qualifications

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• Experience setting architecture standards, reference patterns or technical governance in a complex, matrixed organization.
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• Experience working across multiple business units or brands with different platforms, data boundaries and operating practices.
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• Experience in sports, media, entertainment, hospitality or live events, or with organizations managing large content libraries, talent data and fan-facing products.
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Leadership Attributes

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• Opinionated and decisive: makes the technical call, explains the tradeoffs and owns the recommendation.
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• Systems thinker: designs durable patterns that work across platforms rather than solving one use case at a time.
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• Earned authority: influences senior engineers, security leaders, vendors and business stakeholders without relying on direct reporting lines.
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• Low ego, high standards: challenges weak technical reasoning while working constructively with partner teams.
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• Comfortable with ambiguity: builds the architecture and operating standards as AI scales rather than waiting for a finished model.
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• Every AI portfolio decision has clear technical input on architecture, feasibility, effort, total cost and technical risk.
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