AI Architect (Hybrid)

RTX•Cedar Rapids, IA

1d•$132,400 - $251,600•Hybrid

About The Position

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.

Requirements

A University Degree in Computer Science, Artificial Intelligence, Machine Learning, Engineering, or a related STEM discipline and a minimum of 10 years of relevant professional experience, or an Advanced Degree in a related field and a minimum of 7 years of relevant professional experience.
A minimum of 5 years of experience designing, developing, integrating, or architecting AI/ML solutions, including experience taking AI or ML capabilities beyond experimentation into production environments.
Experience serving as a technical architect, solution architect, technical lead, or senior engineer for complex enterprise software, data, cloud, or AI/ML systems.
Technical experience with AI/ML systems and modern AI application architectures, including Generative AI and large language models.
Experience designing distributed systems, APIs, microservices, enterprise integrations, data pipelines, or cloud-native applications using at least one major public cloud platform.
Experience translating business and technical requirements into architecture designs and evaluating technical, business, cost, security, and operational tradeoffs.
Experience working with enterprise security concepts including identity and access management, authentication and authorization, data protection, application security, and secure system integration.

Nice To Haves

Experience architecting production Generative AI, retrieval-augmented generation, agentic AI, or multi-agent systems including modern AI architecture patterns including model selection and routing, embeddings, vector and enterprise search, context engineering, structured outputs, tool calling, orchestration, memory, state, and human-in-the-loop workflows.
Experience with emerging agent technologies and interoperability approaches such as Model Context Protocol (MCP), agent identity, secure tool integration, or similar standards.
Experience with AI evaluation, observability, tracing, guardrails, model monitoring, Responsible AI, model governance, or production AI reliability and with traditional machine learning lifecycle capabilities including data pipelines, feature engineering, model serving, model registries, monitoring, and MLOps.
Experience designing AI architectures across multiple models, vendors, platforms, and cloud, hybrid, on-premises, or restricted environments.
Experience with Kubernetes, containers, CI/CD, infrastructure-as-code, and modern application deployment architectures.
Familiarity with relevant AI risk, cloud architecture, and enterprise architecture frameworks and principles, such as NIST AI RMF, cloud well-architected frameworks, TOGAF, Zachman, or similar disciplines, and experience applying them to enterprise technology decisions.
Demonstrated ability to lead technical discussions, influence architecture decisions across multidisciplinary teams, and communicate complex technical concepts to technical and non-technical stakeholders.

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 guardrails, and identify common requirements across RTX business units that can be addressed through reusable enterprise AI capabilities.
Architect modern AI solutions including model selection and routing, retrieval and grounding, context engineering, agent orchestration, tool use, state and memory, human-in-the-loop workflows, evaluation, observability, and secure enterprise integration.
Define architecture patterns for AI/ML data pipelines, model serving, model lifecycle management, MLOps, deployment, monitoring, and operation across cloud, hybrid, on-premises, and restricted environments.
Evaluate technology and platform alternatives and lead build, buy, configure, and integrate decisions considering business value, scalability, interoperability, security, performance, cost, and operational complexity.
Lead architecture and technical design reviews, partner with enterprise architecture, cybersecurity, data, identity, privacy, and Responsible AI teams, and provide technical leadership and mentorship across engineering teams.