AI Architect (Hybrid)

About the role

At RTX, the world's largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world’s most complex problems. With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world. Join us and help shape the future of aerospace and defense. The following position is to join our RTX Enterprise Services team: We are seeking an experienced AI Architect to define and guide the architecture of enterprise Artificial Intelligence and Machine Learning solutions across RTX. This role will partner with business units, product teams, engineering organizations, domain architects, cybersecurity, data, and enterprise technology teams to translate complex business needs into scalable, secure, and production-ready AI architectures. The ideal candidate combines deep AI/ML expertise with strong software, data, cloud, and enterprise architecture experience and has demonstrated success guiding complex technology solutions from concept through production. A key focus of this role will be identifying common needs across RTX business units and translating them into reusable enterprise AI capabilities, reference architectures, and design patterns that accelerate adoption while reducing duplication and technical complexity.

Responsibilities

• Define end-to-end architectures for enterprise AI and ML solutions spanning traditional machine learning, Generative AI, agentic AI, data, applications, APIs, platforms, infrastructure, and enterprise systems.
• Partner with business and technology leaders to translate business opportunities and requirements into architecture blueprints, technical strategies, success criteria, and implementation roadmaps.
• Determine the appropriate technical approach for complex business problems, including traditional software, machine learning, Generative AI, retrieval-augmented generation, agentic AI, or combinations of these approaches.
• Develop reusable reference architectures, design patterns, standards, and technical guard