Lead AI Systems Engineer

Responsibilities:

• Build AI systems that directly operate and optimize production networks at massive scale.
• Influence customer experience for millions of broadband and Wi‑Fi users.
• Partner with experts across networking, platform engineering, and AI disciplines.
• Help shape the future of AI-driven operations and intelligent automation at AT&T.
• Play a key role in delivering next‑generation connectivity experiences, including Wi‑Fi 7 and emerging broadband technologies.
• Design, develop, and deploy AI-driven operational platforms leveraging LLMs, automation frameworks, and large‑scale data pipelines.
• Build intelligent solutions that detect anomalies, identify root causes, and automate issue triage across gateway and broadband environments.
• Develop LLM-based applications that analyze logs, telemetry, and operational data to generate actionable insights.
• Create Retrieval‑Augmented Generation (RAG), prompt‑engineering, and agentic AI workflows that support engineering and operational teams.
• Develop and maintain production services that process high‑volume telemetry and operational datasets.
• Integrate AI capabilities into CI/CD pipelines, release validation processes, and incident management workflows.
• Collaborate with network, Wi‑Fi, and platform engineering teams to translate domain expertise into scalable AI solutions.
• Ensure systems are reliable, observable, secure, and production‑ready.
• Continuously improve AI models, workflows, and operational effectiveness based on real‑world feedback and outcomes.

Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• 5+ years of experience in software engineering, systems engineering, platform engineering, or equivalent technical roles.
• Strong proficiency in Python and modern software development practices.
• Experience designing and operating production‑grade distributed systems, services, or data pipelines.
• Hands‑on experience implementing Generative AI solutions, including LLMs, RAG architectures, prompt engineering, and AI workflows.
• Strong understanding of system reliability, observability, debugging, and operational excellence.

Preferred Qualifications

• Experience with real‑time analytics and telemetry platforms such as Apache Pinot or similar technologies.
• Experience applying AI/ML solutions to operational challenges, including AIOps, incident management, or log analysis.
• Familiarity with cloud‑native architectures and distributed computing environments.
• Experience integrating AI into software engineering workflows, including CI/CD and automated testing.
• Knowledge of networking technologies, Wi‑Fi systems, or large‑scale connected device ecosystems.
• Demonstrated ability to solve ambiguous operational problems through scalable technical solutions.