Engineer, System Architecture - AI Enabled Automation

T-Mobile T-Mobile · Telecom · Bellevue, WA +3

This role focuses on developing and enhancing technical network and service architectures by embedding AI-enabled automation into production network workflows. The engineer will design scalable automation solutions using Python, APIs, and data pipelines, interpret operational data to improve automation maturity, and reduce manual intervention. Success is measured by improvements in workflow completion time, system integration, documentation clarity, automation coverage, and service reliability.

What you'd actually do

  1. Design and embed AI-enabled automation directly into network and service architectures
  2. Identify and implement automation use cases across the end-to-end workflow lifecycle
  3. Partner with engineering, operations, and data teams to operationalize AI solutions
  4. Evaluate emerging technologies and improve existing network architectures
  5. Evaluate and enhance AI platforms, automation frameworks, and data pipelines
  6. Define and track measurable performance metrics (10%)

Skills

Required

  • Python
  • APIs
  • data pipelines
  • workflow tools
  • scripting languages
  • lightweight web frameworks
  • database technologies
  • machine learning fundamentals
  • prompt-assisted coding
  • automation frameworks
  • governance considerations
  • workflow automation
  • systems integration
  • analytical skills
  • problem solving
  • communication skills
  • teamwork

Nice to have

  • IMS
  • PSTN connectivity
  • JIRA
  • ServiceNow
  • Flask
  • MySQL
  • telecom
  • VoIP
  • large-scale network environments

What the JD emphasized

  • AI-enabled automation
  • automation solutions
  • AI platforms
  • automation frameworks
  • data pipelines

Other signals

  • embedding AI-enabled automation directly into production network workflows
  • improve automation maturity and reduce manual intervention
  • apply AI to accelerate analysis, decision-making, and deployment efficiency
  • operationalize AI solutions
  • evaluate emerging technologies and improve existing network architectures
  • evaluate and enhance AI platforms, automation frameworks, and data pipelines
  • define and track measurable performance metrics