Director-technology, Enterprise Agentic Solutions

AT&T AT&T · Telecom · Dallas, TX

Director-Technology role focused on leading and scaling engineering teams to build and deliver enterprise agentic AI solutions and platforms. Requires hands-on technical depth in AI agent architectures, LLMs, and Python systems, along with strong leadership experience in managing AI-native delivery from concept to production.

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. Guide teams on context management strategies including session memory, retrieval-augmented generation (RAG), vector search, and user personalization.
  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

  • Background in information retrieval, search, knowledge graphs, or knowledge management systems
  • Experience with advanced agent coordination patterns, multi-agent workflows, or model composition platforms
  • Contributions to open-source LLM, agent, or prompt engineering projects
  • Experience leading teams through organizational transformation from contractor-heavy to in-house delivery models

What the JD emphasized

  • enterprise scale
  • production environments
  • enterprise agentic AI solutions
  • AI-native delivery teams from concept through production at enterprise scale
  • production-grade deployment
  • production support for AI/ML systems

Other signals

  • leading and scaling delivery teams building enterprise agentic AI solutions
  • own end-to-end execution for AI-native products and platforms
  • hands-on technical depth in AI agent architectures, LLMs, and fullstack Python systems
  • proven experience leading high-performing engineering teams that deliver agentic solutions at scale