Principal AI Engineer (SDLC)
Generative AI & Enterprise Applications
About the Role
AT&T is seeking an innovative and results-driven AI Engineer to design, build, and deploy next-generation AI-powered products and platforms across the enterprise. In this role, you will take cutting-edge AI capabilities, including Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), and agentic workflows, and transform them into scalable, production-ready business applications.
Unlike traditional AI research roles that focus on inventing models, this position focuses on making AI usable at scale by integrating AI technologies into enterprise software solutions through the full Software Development Life Cycle (SDLC). You will collaborate with software engineers, architects, product teams, and business stakeholders to deliver secure, reliable, and highly scalable AI solutions that drive business value.
This role is ideal for engineers who are passionate about application development, cloud technologies, APIs, automation, and delivering AI-driven experiences that solve real-world business challenges.
Key Responsibilities
AI Product Development
- Design, develop, and deploy AI-powered applications and platforms that support enterprise business objectives.
- Build Generative AI solutions using Large Language Models (LLMs), RAG architectures, and agent-based workflows.
- Develop intelligent assistants, copilots, chatbots, and AI automation solutions that enhance employee and customer experiences.
- Transform AI concepts, proofs of concept, and prototypes into production-ready software products.
Software Engineering & Integration
- Design and build scalable APIs, microservices, and backend services that power 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.
Cloud & Platform Engineering
- 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 capable of supporting high-volume production workloads.
- Optimize performance, reliability, security, and scalability of AI services.
AI Operations & Delivery
- Establish and maintain CI/CD pipelines for AI-enabled applications and services.
- Implement 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.
Innovation & Collaboration
- 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.
Example Projects You May Build
- 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
An AI Engineer may build a Network Operations Copilot that:
- Leverages multiple Large Language Models (LLMs)
- Integrates with trouble-ticket and operational systems
- Provides intelligent outage recommendations
- Surfaces network analytics and operational insights
- Delivers a fully deployed, production-ready software solution
Required Qualifications
- 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, 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
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
- AI Platform Engineer
Weekly Hours:
40
Time Type:
Regular
Location:
Atlanta, Georgia, Dallas, Texas
