Principal Genai Software Engineer

T-Mobile T-Mobile · Telecom · Bellevue, WA +1

This Principal GenAI Software Engineer role focuses on designing, implementing, and deploying scalable generative AI-enabled solutions, including LLM orchestration, agent creation, and RAG. The role involves providing technical leadership, establishing GenAI engineering standards, and influencing technology decisions to improve operational efficiency and customer experience.

What you'd actually do

  1. Drive the architecture, design, and production deployment of enterprise-scale software development releases using generative AI systems, including LLM orchestration, Agent/Skill creation, retrieval-augmented generation, evaluation, and operational monitoring.
  2. Design and implement modular AI agents that connect to our core systems Create clean abstractions that let non-technical users to leverage these agents into useful workflows without writing code.
  3. Provide technical leadership by mentoring engineers and influencing technology and policy decisions across the organization
  4. Establish GenAI engineering standards, reference architectures, and guardrails to ensure scalability, security, cost efficiency, and responsible AI use across multiple teams. Create templates, patterns, and documentation that accelerate everyone's ability to deliver.
  5. Develop innovative generative-AI enabled software designs and improvements that enhance existing systems and processes

Skills

Required

  • Bachelor's Degree plus 7 years of related work experience OR Advanced degree with 5 years of related experience
  • Computer Science or Engineering
  • 7-10 years Technical engineering experience
  • Communication
  • Customer Service
  • Analytics
  • Technical Writing

Nice to have

  • Proven track record of architecting and scaling enterprise-grade GenAI platforms (LLM orchestration, agent frameworks) that serve multiple teams and operate reliably in production.
  • Experience establishing AI engineering standards, reference architectures, and governance models adopted across an organization.
  • Demonstrated ability to design composable AI systems and internal developer platforms that enable non-technical users to build workflows without engineering support.
  • Recognized technical leader with experience influencing cross-functional strategy, mentoring senior engineers, and driving AI adoption at the organizational level.
  • Experience with release management & CI/CD deployment best practices

What the JD emphasized

  • architecting and scaling enterprise-grade GenAI platforms
  • LLM orchestration
  • agent frameworks
  • serve multiple teams
  • operate reliably in production
  • AI engineering standards
  • reference architectures
  • governance models
  • composable AI systems
  • internal developer platforms
  • non-technical users
  • technical leader
  • influencing cross-functional strategy
  • mentoring senior engineers
  • driving AI adoption at the organizational level

Other signals

  • designing and deploying scalable software generative AI-enabled solutions
  • LLM orchestration
  • Agent/Skill creation
  • retrieval-augmented generation
  • technical leadership
  • GenAI engineering standards
  • reference architectures
  • guardrails
  • responsible AI use