Principal AI Architect

DIRECTV•El Segundo, CA

19h•$134,363 - $244,036•Remote

About The Position

The Principal AI Architect serves as a senior individual contributor responsible for defining, designing, and evolving enterprise-grade Generative AI and AI-driven architectures aligned with DIRECTV’s strategic business objectives. This role provides deep technical leadership and architectural authority across GenAI, AI/ML platforms, and enterprise systems, ensuring solutions are scalable, secure, reusable, and future-ready. The role partners closely with Application Architects, Data Architects, Platform Engineers, and Product teams to embed Generative AI capabilities into enterprise solutions while maintaining strong architectural governance. The architect defines advanced AI architectures including Model Context Protocol (MCP), multi-agent systems, and agent-to-agent orchestration frameworks to enable autonomous, scalable, and governable AI capabilities across the enterprise.

Requirements

Bachelor of Science degree in Computer Engineering, Computer Science, Applied Science, Electrical Engineering, or Math; or equivalent experience.
Technical Career Pathway (TCP) role.
5 – 7 years required, 8+ years desired of experience in Enterprise and Solution Architecture roles.
Deep expertise in Generative AI and AI/ML architectures, including LLM-based systems, RAG, vector stores, orchestration layers, and AI platform design.
Proven experience designing multi-agent systems, agent orchestration frameworks, or context-driven GenAI platforms in enterprise environments.
Demonstrated ability to design secure, scalable, and resilient enterprise architectures.

Nice To Haves

Experience applying Generative AI in enterprise-scale, production environments.
Familiarity with Media and Telecom industry architectures and constraints.
Experience developing or designing PCI-compliant application architecture.
Hands-on architectural experience with Salesforce, MuleSoft, Genesys, and enterprise GenAI platforms or frameworks.
Knowledge of AI governance, responsible AI practices, data privacy, and regulatory considerations.

Responsibilities

Serve as a technical authority for Generative AI and AI-driven architecture across the enterprise, providing hands-on architectural leadership as an individual contributor.
Drive GenAI strategy and solution design, identifying, evaluating, and applying emerging technologies such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, vector databases, prompt engineering frameworks, and model orchestration platforms.
Architect and govern Model Context Protocol (MCP) implementations, defining how context, memory, tools, policies, and guardrails are structured, versioned, and securely shared across AI workflows.
Design and lead multi-agent and agent-to-agent orchestration architectures, enabling autonomous task decomposition, collaboration, escalation, and decision-making across AI agents and enterprise systems.
Define orchestration strategies coordinating LLMs, agents, tools, workflows, and downstream systems, balancing performance, cost, reliability, observability, and maintainability.
Partner with business and product stakeholders to translate business problems into AI-driven solutions, articulate technology value propositions, and support business-case development with measurable outcomes.
Lead architectural discovery and design workshops, aligning AI capabilities with DIRECTV’s strategic goals and guiding teams toward practical, scalable implementations.
Establish reusable AI architecture patterns, reference architectures, and best practices that accelerate adoption while ensuring consistency, governance, and risk management.
Ensure architectural flexibility and future-proofing through modular, abstracted designs that support evolving models, vendors, and regulatory requirements.
Influence enterprise standards for AI architecture, modeling practices, design principles, security, data governance, and responsible AI usage.
Provide architectural guidance to delivery teams, reviewing designs, validating implementation approaches, and ensuring adherence to enterprise and AI-specific standards.