Principal Ai-native Software Engineer

AT&T AT&T · Telecom · USA:TX:Dallas +1

Principal AI-Native Software Engineer at AT&T, blending fullstack Python engineering with hands-on experience building intelligent systems powered by LLMs, agent orchestration, and AI-augmented development workflows. The role involves partnering with business stakeholders, designing and orchestrating intelligent workflows using LLMs and multi-agent systems, developing prompts and retrieval pipelines, and using AI coding assistants. It requires strong Python development, API integration, LLM expertise, and experience with agent orchestration frameworks.

What you'd actually do

  1. Partner directly with business stakeholders to understand needs, refine ideas, and translate goals into working software solutions.
  2. Operate in a collaborative “2-in-a-box” model, co-owning outcomes from ideation through delivery.
  3. Build and integrate AI-powered applications using Python and frameworks such as FastAPI, Flask, or Django.
  4. Design and orchestrate intelligent workflows using LLMs, LangGraph, MCP, and multi-agent systems.
  5. Develop and optimize prompts, context strategies, session memory, retrieval pipelines, and vector-based search to improve agent performance.

Skills

Required

  • fullstack Python development
  • FastAPI, Flask, or Django
  • REST APIs
  • SQL and/or NoSQL
  • LLMs
  • prompt engineering
  • context engineering
  • session management
  • retrieval strategies
  • vector search
  • LangGraph, MCP, or similar agent orchestration frameworks
  • AWS, GCP, or Azure
  • Docker
  • CI/CD pipelines
  • testing frameworks
  • production deployment practices
  • secure, scalable, and maintainable software design

Nice to have

  • AI-augmented development workflows
  • AI coding assistants

What the JD emphasized

  • AI-Native Software Engineer
  • fully integrated AI into how they design, code, test, debug, and deliver solutions
  • AI-augmented development workflows
  • AI coding assistants and agentic development tools as a core part of your workflow
  • Deep, practical use of AI tools in software development

Other signals

  • AI-Native Software Engineer
  • integrated AI into how they design, code, test, debug, and deliver solutions
  • build scalable AI-enabled systems
  • orchestrate intelligent workflows using LLMs, LangGraph, MCP, and multi-agent systems
  • develop and optimize prompts, context strategies, session memory, retrieval pipelines, and vector-based search
  • use AI coding assistants and agentic development tools as a core part of your workflow