At AT&T we’re redefining the future of communication by connecting people to greater possibility - with expertise, simplicity, and inspiration. At the heart of our purpose lies a diverse workforce of 200,000 people and a culture that aspires to serve customers first, act boldly, move faster, and win as one.

Our Product Development group, part of AT&T’s Technology Services (ATS) organization, is responsible for building software-based products, services, and platforms that our customers love and need. Harnessing technology and rebuilding software expertise, the team is inspiring simplicity with projects that deliver revenue and cost savings opportunities.

Job Overview

AT&T is building a new era of intelligent automation. We are looking for a highly skilled LLM/Prompt-Context Engineer with a strong fullstack Python background to design, develop, and integrate intelligent systems focused on large language models (LLMs), prompt engineering, and advanced context management. In this role, you will play a critical part in architecting context-rich AI solutions, crafting effective prompts, and ensuring seamless agent interactions using frameworks like LangGraph.

As a Senior AI Automation Engineer, you will architect, build, and deploy context-driven AI solutions that transform how entire organizations work and deliver value. You’llleverage the power of LLMs, Retrieval-Augmented Generation (RAG), and next-gen AI agents to automate complex workflows, unleashing revolutionary productivity and efficiency. Your work will directly drive the adoption of autonomous AI agents, enabling smarter operations, seamless data pipelines, and adaptive digital services.

Core Responsibilities

• Design, optimize, and evaluate prompts for LLMs to achieve precise and contextually appropriate outputs across diverse use cases.

• Architect and implement dynamic context management strategies, including session memory, retrieval-augmented generation, and user personalization to enhance agent performance.

• Integrate, fine-tune, and orchestrate LLMs within Python-based applications, leveraging APIs and custom pipelines for scalable deployment.

• Build and manage complex conversational and agent workflows using frameworks like LangGraph to support multi-agent or multi-step solutions.

• Develop and maintain robust backend services, APIs, and (optionally) front-end interfaces to deliver end-to-end AI applications.

• Work closely with product, data science, and engineering teams to define requirements, run prompt experiments, and iterate quickly on solutions.

• Implement testing, monitoring, and evaluation pipelines to continuously improve prompt effectiveness, context handling, and overall agent performance.

• Apply and advocate best practices in software engineering, code quality, testing, DevOps, and secure, ethical AI automation.

Skills, Knowledge, and Experience

Day-to-Day

• Design and develop agentic AI solutions and intelligent automation workflows using LLMs, prompt engineering, and context management.

• Collaborate with cross-functional teams to define and refine requirements, run prompt experiments, and iterate on AI-driven solutions.

• Architect and integrate scalable, production-grade Python applications for AI agent deployment.

• Participate in code reviews, contribute to best practices, and mentor peers on AI automation technologies and frameworks.

• Monitor, evaluate, and optimize prompt and context strategies for improved agent performance.

Technical Skills

• Deep experience with full-stack Python development (FastAPI, Flask, Django; SQL/NoSQL databases).

• Demonstrated expertise in prompt engineering for LLMs (OpenAI, Anthropic, open-source LLMs).

• Strong understanding of context engineering, including session management, vector search, and knowledge retrieval strategies.

• Hands-on experience integrating AI agents and LLMs into production systems.

• Proficiency with agentic/conversational flow frameworks such as LangGraph.

• Familiarity with Retrieval-Augmented Generation (RAG) pipelines and multi-agent orchestration.

• Experience with cloud infrastructure, containerization (Docker), and CI/CD practices.

• Exceptional analytical, problem-solving, and communication skills.

Additional Desirable Experience

• Experience evaluating and fine-tuning LLMs or working with RAG architectures.

• Background in information retrieval, search, or knowledge management systems.

• Contributions to open-source LLM, agent, or prompt engineering projects.

• Experience with secure, ethical, and scalable deployment of AI solutions in enterprise environments.