Date Posted:
2026-09-17
Country:
United States of America
Location:
US-CT-FARMINGTON-0004 ~ 4 Farm Springs Rd ~ 4 FARM SPRINGS
Position Role Type:
Hybrid
U.S. Citizen, U.S. Person, or Immigration Status Requirements:
This job requires a U.S. Person. A U.S. Person is a lawful permanent resident as defined in 8 U.S.C. 1101(a)(20) or who is a protected individual as defined by 8 U.S.C. 1324b(a)(3). U.S. citizens, U.S. nationals, U.S. permanent residents, or individuals granted refugee or asylee status in the U.S. are considered U.S. persons. For a complete definition of "U.S. Person" go here. https://www.ecfr.gov/current/title-22/chapter-I/subchapter-M/part-120/subpart-C/section-120.62
Security Clearance Type:
None/Not Required
Security Clearance Status:
Not Required

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.

What You Will Do

• 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.

What You Will Learn

• How AI and ML technologies are applied across a global aerospace and defense enterprise spanning diverse business, engineering, manufacturing, and operational domains.
• How enterprise AI platforms and reusable architecture patterns enable AI solutions to scale across multiple business units while meeting security, data, and governance requirements.
• How Generative AI and agentic AI capabilities are evolving from experimentation into production systems that interact with enterprise data, tools, applications, and workflows.
• How AI architectures are designed across commercial cloud, hybrid, on-premises, and restricted computing environments.
• How emerging AI technologies, models, frameworks, interoperability standards, and industry practices can be evaluated and translated into practical enterprise capabilities.
• How to influence the technical direction of enterprise AI by working across business units, architecture disciplines, engineering organizations, and senior leadership.

Qualifications You Must Have

• 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.

Qualifications We Prefer

• 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.

What We Offer

Whether you're just starting out on your career journey or are an experienced professional, we offer a robust total rewards package with compensation; healthcare, wellness, retirement and work/life benefits; career development and recognition programs. Some of the benefits we offer include parental (including paternal) leave, flexible work schedules, achievement awards, educational assistance and child/adult backup care.

Learn More & Apply Now!

Work Location: This is a hybrid role, eligible candidates must reside within commuting distance of Farmington, CT, El Segundo, CA, San Jose, CA, Tucson, AZ, McKinney, TX, Andover, MA, Cedar Rapids, IA, or Charlotte, NC.

Please ensure the role type defined below is appropriate for your needs before applying to this role. This position is classified as:

Hybrid: Employees who are working in Hybrid roles will work regularly both onsite and offsite. Ratio of time working onsite will be determined in partnership with your leader.

As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote.

The salary range for this role is 132,400 USD - 251,600 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills.

Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement.

Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company's performance.

This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply.

RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window.