Software Engineer - Backend [T500-28885]

About T-Mobile:T-Mobile US, Inc. (NASDAQ: TMUS), headquartered in Bellevue, Washington, is America’s supercharged Un-carrier, connecting millions through its strong nationwide network and flagship brands, T-Mobile and Metro by T-Mobile. Customers benefit from an unmatched combination of value, quality, and exceptional service experience.TMUS Global Solutions:TMUS Global Solutions is a world-class technology powerhouse accelerating the company’s global digital transformation. With a culture built on growth, inclusivity, and global collaboration, the teams here drive innovation at scale, powered by bold thinking.About the Role:The Software Engineer is responsible for designing, building, deploying, operating, and continuously improving production software platforms that support consumer-facing AI applications and cloud-native services. This role follows an end-to-end software ownership model where engineers are accountable for the full software development lifecycle — from architecture and implementation through deployment, production support, observability, and continuous improvement.Working closely with AI Platform Engineers, Product Owners, Architecture, and Platform teams, this role develops reliable, scalable software while ensuring operational excellence across production environments. The ideal candidate is a strong software engineer with experience building cloud-native distributed systems and a willingness to own software in production.What You'll Do:Design, develop, and maintain backend services, APIs, microservices, and cloud-native applications supporting AI platform capabilities.Own software throughout its lifecycle, including design, implementation, deployment, production support, monitoring, and continuous improvement.Build scalable, resilient, and secure distributed systems using modern software engineering practices.Collaborate with AI Platform Engineers to deliver production-ready platform services supporting conversational AI, agentic AI, and consumer-facing AI applications.Develop and maintain CI/CD pipelines that enable reliable, automated software delivery.Deploy and operate containerized applications using Kubernetes and cloud-native platforms.Participate in production support, troubleshooting, root cause analysis, and post-incident improvements.Implement application monitoring, logging, and observability practices to improve software reliability and operational visibility.Drive software engineering best practices through architecture discussions, code reviews, automated testing, and mentoring junior engineers.Partner with Platform Engineering teams to continuously improve reliability, scalability, security, and developer productivity.What You'll Bring:Bachelor's degree in computer science, Software Engineering, Information Systems, or related field, or equivalent practical experience.4+ years of software engineering experience building and operating cloud-native production software.Strong programming experience in Java, Python, or Go.Experience designing and developing distributed systems, microservices, REST APIs, and event-driven applications.Experience building cloud-native applications using Kubernetes and container technologies.Experience implementing CI/CD pipelines and automated software delivery practices.Experience supporting production software applications, troubleshooting complex issues, and driving operational improvements.Strong understanding of software architecture, scalability, resiliency, performance optimization, and secure software development.Experience collaborating across distributed Agile engineering teams.Excellent communication, technical leadership, and mentoring skills.Must Have Skills:Software engineering leadership and technical mentoringJava, Python, or GoDistributed systems and microservicesREST APIs and backend software architectureCloud-native software developmentKubernetes application deployment and operationsCI/CD and software delivery automationProduction software support and troubleshootingSDLC and Agile engineering practicesObservability and application monitoring conceptsNice to Have:Familiarity with Infrastructure-as-Code (Terraform or similar)Experience with monitoring and observability platforms such as Prometheus, Grafana, Splunk, Datadog, or OpenTelemetryFamiliarity with reliability engineering concepts including SLIs, SLOs, and error budgetsExperience supporting AI platforms, LLM-powered services, or conversational AI applicationsExperience with Model Context Protocol (MCP), API gateways, or enterprise integration patternsExperience working with Agentic AI platform teams or consumer-facing AI application teams