Lead Enterprise AI Engineer — LLMs, RAG & Production

AT&T; Dallas (TX); On-site; USD 170,000 - 230,000; Full time

Job summary

AT&T is actively seeking an innovative Principal AI Engineer to design, build, and deploy AI-powered products across the enterprise. You will work with LLMs, RAG architectures, and agentic workflows to deliver scalable, production-ready software that integrates AI into enterprise platforms.

The role emphasizes SDLC-driven development, collaboration with cross-functional teams, API and microservice design, and cloud-native deployment across Azure and AWS.

Qualifications

• Bachelor's degree in a technical field.
• Experience building enterprise applications with modern software practices.
• Strong Python and modern app development framework skills.
• Experience building RESTful APIs and microservices.
• Knowledge of Generative AI, LLMs, and AI architectures.
• Experience with Azure and/or AWS.
• Experience in Agile and SDLC environments.
• Knowledge of CI/CD, DevOps, and release management.
• Experience with containerization/orchestration (Kubernetes, Docker).
• Strong problem-solving, analytical, and collaboration skills.

Responsibilities

• Design, develop, and deploy AI-powered applications and platforms for enterprise objectives.
• Build Generative AI solutions using LLMs, RAG architectures, and agent-based workflows.
• Develop intelligent assistants, copilots, chatbots, and AI automation for employees and customers.
• Transform proofs of concept into production-ready software products.

Skills

Python

RESTful APIs

Generative AI

LLMs

AI architectures

Azure/AWS

Agile & SDLC

CI/CD

DevOps

Kubernetes

Docker

Vector databases

Prompt engineering

AI evaluation frameworks

Problem-solving

Education

Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical field

Tools

Kubernetes

Docker

Job description

AT&T is actively seeking an innovative Principal AI Engineer to design, build, and deploy AI-powered products across the enterprise. You will work with LLMs, RAG architectures, and agentic workflows to deliver scalable, production-ready software that integrates AI into enterprise platforms.

The role emphasizes SDLC-driven development, collaboration with cross-functional teams, API and microservice design, and cloud-native deployment across Azure and AWS.