AI Engineering Internship: GenAI, LLMs & Pipelines

Infineon Technologies AG; Singapore; On-site; SGD 11,000 - 22,000; Full time

Job summary

Infineon Technologies AG in Singapore invites an AI Engineering intern to contribute to Agentic AI, Hybrid Customer Journey optimization, and Supply Chain & Quality Analytics. You will gain hands-on experience designing, developing, and deploying advanced AI solutions creating measurable business impact.

You will collaborate with AI engineers and data scientists, analyze data, build data pipelines, and contribute to scalable AI systems while learning enterprise AI lifecycle, analytics platforms,

Qualifications

• Pursuing a Bachelor's or Master's in CS, AI, DS, ML or related field.
• Strong Python programming fundamentals required.
• Hands-on GenAI, LLMs, RAG and AI app development experience.
• Familiar with PyTorch, TensorFlow and AI tooling.

Responsibilities

• Collaborate with AI engineers on ML/DL projects and LLMs.
• Apply statistics, ML, and visualization to big datasets.
• Design, develop, and deploy production-grade AI solutions.
• Build and maintain AI-enabled applications for business processes.
• Contribute to scalable, real-time AI systems and data quality.
• Support operations through debugging and improvements.
• Develop data pipelines to ingest and process diverse data sources.

Skills

Python

GenAI & Agentic AI

AI architecture

Analytical thinking

Communication skills

Education

Bachelor's or Master's in Computer Science / AI / Data Science / ML or related

Tools

PyTorch

TensorFlow

LangChain

LangGraph

REST APIs

Docker/OpenShift

AWS/Azure/GCP

Job description

Infineon Technologies AG in Singapore invites an AI Engineering intern to contribute to Agentic AI, Hybrid Customer Journey optimization, and Supply Chain & Quality Analytics. You will gain hands-on experience designing, developing, and deploying advanced AI solutions creating measurable business impact.

You will collaborate with AI engineers and data scientists, analyze data, build data pipelines, and contribute to scalable AI systems while learning enterprise AI lifecycle, analytics platforms,