AI Engineering Intern: Generative AI & Enterprise ML

Infineon Technologies AG; Singapore; On-site; SGD 17,000 - 26,000; Full time

Job summary

Infineon Technologies AG in Singapore offers an AI Engineering internship within the Center of Competence Analytics. You will contribute to AI, data analytics, and enterprise AI workflows, gaining hands-on experience with GenAI, LLMs, RAG, and data pipelines.

You will collaborate with AI engineers and data scientists, design production-ready analytics solutions, build AI-enabled applications, and learn enterprise AI lifecycle while developing strong software engineering practices.

Qualifications

• Pursuing a Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or related field.
• Strong programming skills in Python and software engineering fundamentals.
• Hands-on experience with Generative AI, LLMs, Agentic AI, Retrieval Augmented Generation (RAG), and AI app development.

Responsibilities

• Collaborate with AI engineers and data scientists on ML/DL/LLM projects.
• Apply statistical analysis and data visualization to reveal insights from large datasets.
• Design, develop, and deploy production-ready AI and analytics solutions.
• Build and maintain AI-enabled applications integrated into business processes.
• Contribute to scalable, real-time AI systems with high data quality.

Skills

Python

Generative AI

LLMs

Agentic AI

Data Analysis

Data Visualization

Education

Bachelor’s or Master’s in CS/AI/Data Science

Tools

PyTorch

TensorFlow

LangChain

LangGraph

REST APIs

Docker

Job description

Infineon Technologies AG in Singapore offers an AI Engineering internship within the Center of Competence Analytics. You will contribute to AI, data analytics, and enterprise AI workflows, gaining hands-on experience with GenAI, LLMs, RAG, and data pipelines.

You will collaborate with AI engineers and data scientists, design production-ready analytics solutions, build AI-enabled applications, and learn enterprise AI lifecycle while developing strong software engineering practices.