AI Platform Engineer (Hybrid)

RTX•Cedar Rapids, IA

7d•$107,500 - $204,500•Hybrid

About The Position

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.

Requirements

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.

Nice To Haves

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.

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.