AI Platform Engineer (Hybrid)
About the role
with paragraphs. - Responsibilities: The bullet list under "What You Will Do". Use- .
- Qualifications: There's "Qualifications You Must Have" and "Qualifications We Prefer". Could combine into Qualifications section? The instruction says use heading Qualifications. We'll include both must have and preferred as subpoints? Could separate but still under Qualifications heading with paragraphs/lists. We'll include must-have as list, preferred as list.
- Skills: Not explicitly separate; but qualifications include skills. Could we create Skills section? Might be redundant. Probably better to not create Skills if not explicit. The instruction: "Prefer a few substantive sections over many thin ones." So we can combine skills into Qualifications.
- Benefits: There's "What We Offer" paragraph describing benefits. Also later details about benefits. So we can have Benefits section.
- Pay: There's salary range: "The salary range for this role is 107,500 USD - 204,500 USD." Also mention short-term/long-term incentive compensation. So we can include Pay section.
- Schedule: There's hybrid work location description and ratio. Also "Work Location: This is a hybrid role..." So we can include Schedule section (maybe hybrid schedule). Also mention "flexible work schedules". So we can include Schedule.
We must not include sections with filler like "commensurate with experience". Not present.
We must remove boilerplate: EEO, privacy policy, etc. Also remove "Learn More & Apply Now!" and application instructions. Also remove "Date Posted", "Country", "Location", "Position Role Type", etc. Also remove security clearance info? It's substantive? It says Security Clearance Type: None/Not Required etc. Might be considered metadata but not required to keep? It's not a heading; we could drop as minor metadata. Probably drop.
We must preserve all substantive information: So we need to keep details about responsibilities, qualifications, benefits, pay, schedule, hybrid location, etc.
Let's craft HTML.
Structure:
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 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.
Responsibilities
- 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.
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.
Preferred Qualifications
- 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.
Benefits
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. 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.
Pay
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.
Schedule
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. 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. Flexible work schedules are offered as part of the benefits.
We must ensure no extra sections like "About the company"? Already covered. Check for any leftover boilerplate: We removed EEO, privacy policy, etc. Also removed "Learn More & Apply Now!" and application instructions. Also removed "Date Posted", "Country", "Location", "Position Role Type", "U.S. Citizen, U.S. Person, or Immigration Status Requirements", "Security Clearance Type", etc. Those are minor metadata; we dropped them. Check that we didn't miss any substantive info: The description of what you will learn (under "What You Will Learn") is substantive; we omitted that! Indeed there is a "What You Will Learn" section after responsibilities. We need to include that somewhere. Could be part of Responsibilities or a separate section like "Learning Opportunities". But we only have predefined headings; we could incorporate into Responsibilities or About the role? It's about what you will learn, which is like a learning outcome. Could be placed under a heading like "What You Will Learn" but not in allowed headings. The instruction says use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings? It says usefor section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It implies we should use those headings for sections; but we could also add other headings if needed? The instruction: "Use
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." It gives examples but not exclusive list. However later: "IMPORTANT: include a section ONLY when the posting has