Software Engineering Intern

Job Description

Software Engineering Intern | T-Mobile

The Tone:
This is an internship at T-Mobile. T-Mobile is a leader in the wireless industry, known for its innovation in customer service and 5G technology. This role is crucial for gaining hands-on experience and contributing to engineering initiatives that enhance automation, AI adoption, and testing efficiency across vital telecom platforms. Interns will develop meaningful skills by working on real-world projects alongside T-Mobile employees and mentors.

The TL;DR
• Role: Internship
• Type: Temporary
• Pay: $26–$47 hourly
• Team: Engineering organization that designs, builds, and supports platforms and tools for telecom testing and automation.
• Mission: Contribute to engineering initiatives that enhance automation, AI adoption, and testing efficiency across telecom platforms.
• Tech Stack: Python, Jenkins, Robo/Robot Framework, Azure OpenAI, RAG workflows, agent frameworks, OpenAI, Claude, Windsurf, PyTorch, TensorFlow, Scikit-learn, Docker, Kubernetes, Azure, AWS, LangChain, LlamaIndex, vector databases.

What You’ll Actually Do
• Design and develop AI-powered automation solutions to improve telecom test efficiency.
• Build Python-based backend components that integrate with CI/CD systems such as Jenkins.
• Develop and evaluate LLM-based solutions for analyzing logs, alarms, and KPI datasets.
• Create intelligent workflows for anomaly detection and root-cause analysis.
• Integrate APIs and vendor platforms into internal orchestration and automation tools.

The Must-Haves
• Background: Must be actively enrolled in a Bachelor’s or Graduate degree program.
• Experience: No specific years of experience are required. Hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn is preferred.
• Skills: Python, CI/CD systems, LLM-based solution development, data analysis, Agile methodologies.
• Bonus: Experience working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) architectures, or agent frameworks; familiarity with AI/ML ecosystems including Azure OpenAI, OpenAI APIs, LangChain, LlamaIndex, or vector databases; exposure to log analysis, anomaly detection, or time-series data processing; experience with containerization and cloud technologies such as Docker, Kubernetes, Azure, or AWS.