Director of Technology, Agentic AI Delivery

This position requires office presence of a minimum of 5 days per week and is only located in the location(s) posted. No relocation is offered.

Join AT&T and help shape the future of communications and technology that connect the world. We value innovators who seek to explore the unknown and challenge the status quo. Bring your bold ideas and fearless spirit to redefine connectivity and transform how people share stories and experiences. At AT&T, you won’t just imagine the future—you’ll build it.

Position Overview

We are seeking a Director - Technology to lead and scale delivery teams building enterprise agentic AI solutions. This leader will own end-to-end execution for AI-native products and platforms, directly managing engineers across multiple disciplines, some to include: LLM/Prompt-Context Engineering, Backend/Agent Engineering, and Integration Engineering. The ideal candidate combines hands-on technical depth in AI agent architectures, LLMs, and fullstack Python systems with proven experience leading high-performing engineering teams that deliver agentic solutions at scale.

This role is responsible for translating strategic AI initiatives into shipped products that drive measurable business value.

What You’ll Do:

Delivery Leadership and Team Management

• Lead, grow, and manage a team of engineers delivering AI and Agentic solutions and products.
• Own end-to-end delivery execution for agentic AI initiatives, from concept through production, ensuring quality, velocity, and operational readiness.
• Set technical direction, engineering standards, and delivery cadences across all workstreams.
• Build and scale durable in-house engineering teams, reducing reliance on contractor resources and improving knowledge retention.
• Drive hiring, performance management, career development, and mentorship for direct reports.

AI-Native Technical Leadership

• Provide architectural oversight for LLM integration, prompt/context engineering, and multi-agent orchestration using frameworks such as LangGraph.
• Guide teams on context management strategies including session memory, retrieval-augmented generation (RAG), vector search, and user personalization.
• Ensure scalable, production-grade deployment of AI agents through robust backend services, APIs, and CI/CD pipelines.
• Drive evaluation, testing, and continuous optimization of prompt effectiveness, agent workflows, and system reliability.

Platform and DevOps Accountability

• Drive DevOps strategy, CI/CD governance, and platform security across delivery workstreams.
• Establish platform security hardening, vulnerability management, and compliance frameworks for AI-native services.
• Ensure architectural consistency, integration standards, and operational excellence across all agentic solution delivery teams.
• Own incident response, RCA, and continuous improvement for reliability, performance, and delivery velocity.

Cross-Functional Execution

• Partner with Product, Architecture, and Business stakeholders to translate requirements into delivered outcomes aligned to strategic priorities.
• Drive cross-functional alignment on technical direction, trade-offs, roadmaps, and delivery status.
• Coordinate with data science, ML engineering, and front-end teams to deliver end-to-end AI-powered applications.

What You’ll Need:

Leadership Experience

• 8+ years of progressive technology leadership, with 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.

Technical Depth (Hands-On Experience Required)

• 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.
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