Director of Technology, Agentic AI Delivery

AT&T AT&T · Telecom · USA:TX:Dallas +3

Director of Technology, Agentic AI Delivery at AT&T, responsible for leading and scaling delivery teams building enterprise agentic AI solutions. This role owns end-to-end execution for AI-native products and platforms, managing engineers in areas like LLM/Prompt-Context Engineering, Backend/Agent Engineering, and Integration Engineering. Requires hands-on technical depth in AI agent architectures, LLMs, and fullstack Python systems, along with proven experience leading high-performing engineering teams that deliver agentic solutions at scale.

What you'd actually do

  1. Lead, grow, and manage a team of engineers delivering AI and Agentic solutions and products.
  2. Own end-to-end delivery execution for agentic AI initiatives, from concept through production, ensuring quality, velocity, and operational readiness.
  3. Provide architectural oversight for LLM integration, prompt/context engineering, and multi-agent orchestration using frameworks such as LangGraph.
  4. Drive evaluation, testing, and continuous optimization of prompt effectiveness, agent workflows, and system reliability.
  5. Partner with Product, Architecture, and Business stakeholders to translate requirements into delivered outcomes aligned to strategic priorities.

Skills

Required

  • 8+ years of progressive technology leadership
  • 3+ years directly managing engineering teams delivering AI/ML or automation solutions
  • Proven track record of leading AI-native delivery teams from concept through production at enterprise scale
  • Experience managing resources across multiple engineering disciplines (backend, fullstack, AI/ML, DevOps)
  • Demonstrated ability to recruit, develop, and retain top engineering talent
  • Deep experience with fullstack Python development (FastAPI, Flask, Django; SQL/NoSQL databases)
  • Demonstrated expertise in prompt engineering and context engineering for LLMs (OpenAI, Anthropic, open-source models)
  • Hands-on experience architecting and deploying AI agents and multi-agent systems in production environments
  • Proficiency with agent orchestration frameworks such as LangGraph
  • Strong understanding of RAG architectures, vector databases, knowledge retrieval strategies, and session/context management
  • Experience with cloud infrastructure (AWS, GCP, Azure), containerization (Docker), and CI/CD pipelines
  • Knowledge of RESTful API design, distributed systems, and scalable backend architectures
  • Experience establishing engineering standards, DevOps practices, and release governance for AI-native platforms
  • Track record of improving delivery predictability, reducing rework, and maintaining platform stability
  • Experience with incident management, observability, and production support for AI/ML systems

Nice to have

  • LangGraph

What the JD emphasized

  • enterprise agentic AI solutions
  • AI-native products and platforms
  • agentic solutions at scale
  • agentic AI initiatives
  • AI agents and multi-agent systems in production environments

Other signals

  • leading enterprise agentic AI solutions
  • own end-to-end execution for AI-native products and platforms
  • managing engineers across multiple disciplines
  • hands-on technical depth in AI agent architectures, LLMs, and fullstack Python systems
  • leading high-performing engineering teams that deliver agentic solutions at scale
  • translating strategic AI initiatives into shipped products