Sr Software Engineer, Agentic AI

T-Mobile T-Mobile · Telecom · Atlanta, GA +2

This role focuses on designing, developing, and deploying scalable software and Agentic AI solutions for intelligent customer engagement. The engineer will build AI-enabled applications, APIs, distributed services, and orchestration layers integrating LLMs, conversational AI, and enterprise systems. Responsibilities include developing multi-step AI workflows, agent-based systems, and contributing to cloud-native microservices and distributed systems for voice and text channels.

What you'd actually do

  1. Design, develop, test, and deploy scalable software and Agentic AI solutions to support enterprise automation, intelligent workflows, and customer engagement platforms
  2. Build and enhance AI-enabled applications, backend services, APIs, and integration components using modern software engineering and cloud-native best practices
  3. Develop and implement multi-step AI workflows, orchestration logic, and agent-based systems leveraging LLMs, RAG architectures, and AI automation frameworks
  4. Contribute to the design of microservices and distributed systems supporting real-time voice and text-based customer interactions at scale
  5. Collaborate with cross-functional engineering, AI, platform, and product teams to deliver secure, reliable, and high-performing AI-driven solutions

Skills

Required

  • Python, Java, or C++
  • cloud-native distributed systems, APIs, microservices, and real-time integration platforms
  • LLMs, conversational AI, agentic AI workflows, and AI orchestration frameworks
  • AI-driven automation, RAG solutions, and enterprise AI integrations
  • scalability, reliability, observability, and secure software engineering best practices for production AI systems

Nice to have

  • Docker, Kubernetes, and scalable microservices architectures
  • Large Language Models (LLMs), prompt engineering, tool orchestration, and AI-assisted development workflows
  • highly available, scalable, and production-grade distributed systems and AI platforms
  • voice and text-based customer engagement and conversational AI solutions
  • AI orchestration frameworks, RAG architectures, and real-time AI integration patterns for enterprise applications

What the JD emphasized

  • Agentic AI solutions
  • Agentic AI
  • agentic AI workflows
  • agent-based systems
  • conversational AI
  • agentic AI workflows
  • conversational AI
  • agentic AI workflows

Other signals

  • designing, developing, and deploying scalable software and Agentic AI solutions
  • build AI-enabled applications, APIs, distributed services, and orchestration layers integrating LLMs
  • develop and implement multi-step AI workflows, orchestration logic, and agent-based systems leveraging LLMs, RAG architectures, and AI automation frameworks