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 Platform Engineer to design, build, and operate the reusable software, services, infrastructure, and runtime capabilities that enable Artificial Intelligence and Machine Learning solutions to be developed, deployed, secured, observed, and scaled across RTX. The ideal candidate combines strong backend software engineering with cloud, DevOps, and AI platform experience. This is a hands-on engineering role focused on building production platform capabilities, not simply deploying infrastructure. You will work closely with AI Architects, Applied AI Engineers, application teams, cybersecurity, data, and enterprise technology organizations to provide secure and reusable capabilities that accelerate AI adoption across RTX.

What You Will Do

• Design, develop, and operate scalable backend services, APIs, and distributed platform capabilities that support enterprise AI applications, models, and agents.
• Build and integrate AI platform capabilities including model access and routing, AI gateways, agent runtimes, lifecycle management, tool integration, model serving, retrieval services, and related enterprise AI services.
• Develop secure capabilities for agent and application identity, authentication and authorization, secrets management, tool access, permissions, and integration with enterprise systems.
• Design and automate deployment across development, test, and production environments using cloud-native technologies, containers, Kubernetes, CI/CD, and infrastructure-as-code.
• Build observability capabilities including logging, metrics, tracing, monitoring, alerting, execution telemetry, and cost visibility for AI applications and agentic workloads.
• Develop platform capabilities supporting AI evaluation, model lifecycle management, MLOps, configuration, versioning, and production operations.
• Design platform services for scalability, availability, resilience, performance, security, and support across commercial cloud, hybrid, on-premises, and restricted environments.
• Partner with AI Architecture, Applied AI, Application Engineering, Cybersecurity, Data, and product teams to translate solution needs into reusable enterprise platform capabilities and continuously improve the developer experience.

What You Will Learn

• How enterprise AI platforms enable Generative AI, machine learning, and agentic applications to operate securely and consistently across a global aerospace and defense company.
• How models, agents, tools, identity, data, evaluation, and observability come together as reusable enterprise platform capabilities.
• How AI workloads are designed and operated across commercial cloud, hybrid, on-premises, and restricted computing environments.
• How production agentic systems introduce new engineering challenges around identity, tool access, runtime governance, state, observability, and operational control.
• How emerging AI platforms, orchestration technologies, interoperability standards, and cloud services can be evaluated and incorporated into enterprise architectures.
• How reusable platform capabilities can reduce duplication and accelerate AI solution delivery across multiple RTX business units.

Qualifications You Must Have

• A University Degree in Computer Science, Software Engineering, Engineering, or a related STEM discipline and a minimum of 8 years of relevant professional experience, or an Advanced Degree in a related field and a minimum of 5 years of relevant professional experience.
• A minimum of 5 years of hands-on software engineering experience developing backend services, APIs, distributed systems, cloud platforms, or similar production software.
• Programming experience using Python, Java, C#, or another modern backend programming language, with demonstrated experience developing tested and maintainable production software.
• Experience designing and building APIs, microservices, distributed services, event-driven systems, or other backend platform capabilities.
• Experience with cloud-native engineering including Docker, Kubernetes, CI/CD, and infrastructure-as-code, and experience deploying applications or services using at least one major public cloud platform.
• Experience with production observability and operations, including logging, metrics, tracing, monitoring, alerting, troubleshooting, and reliability.
• Experience working with enterprise security concepts including authentication and authorization, identity and access management, secrets management, network security, and secure application integration.

Qualifications We Prefer

• Experience building AI/ML platforms, developer platforms, internal platforms, or shared enterprise software services including experience with AI gateways, model serving, inference platforms, model routing, agent runtimes, orchestration platforms, or model lifecycle capabilities.
• Experience with agentic AI platform concepts including agent registration, tool execution, Model Context Protocol (MCP), agent identity, permissions, state, lifecycle management, or agent observability.
• Experience with vector databases, enterprise search, retrieval platforms, knowledge services, feature stores, model registries, or other AI/ML infrastructure and MLOps, model deployment, model versioning, production monitoring, AI evaluation infrastructure, or model lifecycle automation.
• Experience operating highly available Kubernetes or distributed application platforms and implementing resilience, scalability, and disaster-recovery patterns.
• Experience supporting AI or enterprise applications across hybrid cloud, on-premises, restricted, or highly regulated environments and familiarity with AI security, Responsible AI, cloud architecture principles, cost management, FinOps, or enterprise governance requirements.
• Demonstrated ability to independently solve complex technical problems, collaborate across engineering disciplines, and influence technical decisions within large-scale software or platform initiatives.

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 107,500 USD - 204,500 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.