Principal AI Engineer (SDLC)

AT&T seeks a Principal AI Engineer to design, build, and deploy production-ready generative AI and enterprise applications across the full SDLC.

Responsibilities

• Design, develop, and deploy AI-powered applications and platforms aligned to enterprise business objectives
• Build generative AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agent-based workflows
• Develop intelligent assistants including copilots, chatbots, and AI automation solutions to improve employee and customer experiences
• Turn AI concepts, proofs of concept, and prototypes into production-ready software products
• Design and build scalable APIs, microservices, and backend services powering AI-enabled applications
• Integrate AI capabilities into existing enterprise platforms, business systems, and customer-facing applications
• Develop secure, reliable, and reusable application components within modern software architectures
• Partner with cross-functional teams to translate business requirements into technical solutions
• Deploy and manage AI workloads across cloud environments including Azure and AWS
• Implement containerized and cloud-native solutions using Kubernetes and modern orchestration technologies
• Build and maintain enterprise-grade AI platforms designed for high-volume production workloads
• Optimize performance, reliability, security, and scalability of AI services
• Establish and maintain CI/CD pipelines for AI-enabled applications and services
• Apply MLOps best practices for deployment, monitoring, testing, and lifecycle management of AI solutions
• Support model integration, version management, governance, and operational excellence
• Monitor production environments and continuously improve platform performance and user experience
• Evaluate emerging AI technologies and identify opportunities for enterprise adoption
• Collaborate with product managers, software engineers, data scientists, UX teams, and business stakeholders
• Contribute to technical architecture decisions and AI engineering best practices
• Drive continuous improvement across AI development methodologies and delivery frameworks

Requirements

• Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical field
• Experience developing enterprise applications using modern software engineering practices
• Strong proficiency in Python and modern application development frameworks
• Experience building RESTful APIs and microservices
• Knowledge of generative AI technologies including LLMs and AI application architectures
• Experience with cloud platforms such as Azure and/or AWS
• Experience working within Agile and SDLC environments
• Knowledge of CI/CD pipelines, DevOps practices, and software release management
• Experience with containerization and orchestration technologies such as Kubernetes and Docker
• Strong problem-solving, analytical, and collaboration skills

Technologies

• Python
• RESTful APIs
• microservices
• Large Language Models (LLMs)
• Generative AI
• Retrieval-Augmented Generation (RAG)
• agent-based workflows
• Azure
• AWS
• Kubernetes
• Docker
• CI/CD pipelines
• DevOps
• MLOps
• Agile
• SDLC
• vector databases
• prompt engineering
• AI evaluation frameworks
• MLOps platforms
• machine learning deployment practices

Example projects

• Enterprise generative AI chatbots and virtual assistants
• AI-powered recruiting and talent acquisition solutions
• Customer care AI assistants powered by GPT and LLM technologies
• Retrieval-Augmented Generation (RAG) platforms
• Multi-agent and agentic workflow automation systems
• AI APIs and shared enterprise AI services
• AI-enabled operational intelligence and analytics platforms

Example at AT&T

• Build a Network Operations Copilot that leverages multiple LLMs
• Integrate with trouble-ticket and operational systems
• Provide intelligent outage recommendations
• Surface network analytics and operational insights
• Deliver a fully deployed, production-ready software solution

Preferred qualifications

• Experience building RAG (Retrieval-Augmented Generation) solutions
• Experience developing agentic AI workflows and autonomous AI systems
• Knowledge of MLOps platforms and machine learning deployment practices
• Experience integrating AI solutions into enterprise business systems
• Familiarity with vector databases, prompt engineering, and AI evaluation frameworks
• Experience developing scalable cloud-native AI applications
• Exposure to enterprise security, governance, and responsible AI practices

Ideal background

• Candidates may currently hold titles such as AI Engineer, Generative AI Engineer, Machine Learning Engineer, AI Solutions Engineer, AI Application Engineer, or AI Platform Engineer

Location and schedule

• Dallas, TX onsite
• Location listing includes: Atlanta, Georgia and Dallas, Texas
• Weekly hours: 40
• Time type: Regular