Please submit your application via: https://jobs.infineon.com/careers/job/private?domain=infineon.com&pid=563808967589462

This position reports to the Data Scientist Manager and mainly working on sustaining and improving AI applications. Trainee will gain hands-on experience in maintaining, fine-tuning, and optimizing deep learning models for production applications. The Trainee will play a key role to ensure reliability, scalability, and performance of existing AI systems while exploring opportunities for continuous development and enhancement.

Your Role

Key responsibilities in your new role:

• Existing use case sustenance: establish performance monitoring metric for deploy AI models to ensure accuracy and reliability in production environments. identify potential issues or deviations and implement necessary adjustments such as re-training or model fine-tuning
• Enhance AI workflows: Refine datasets and improve data preprocessing pipelines to ensure robust model performance
• Evaluate and validate models: benchmark AI models against KPIs and validate model outputs to ensure adherence to business requirements and mitigate risks
• Collaboration and knowledge-sharing: work closely with cross-functional teams to sustain current AI applications and align with business needs
• Research and development: explore & suggest tools, techniques and technologies to further improve model performance. forward-looking with the latest advancement in AI and apply best practices to existing applications
• Support scaling effort: contribute to the deployment and scaling of AI models across processes and sites

Your Profile

Qualifications and skills to help you succeed:

• Basic to intermediate knowledge of machine learning and image classification techniques (e.g., CNNs, transfer learning, etc.).
• Familiarity with tools such as Python, TensorFlow, PyTorch, or similar machine learning frameworks.
• Experience with image processing libraries like OpenCV, Pillow, or scikit-image is a plus.
• Familiarity with any version control system (e.g., Git) and cloud platforms (e.g., AWS, Azure, or Google Cloud) is a bonus.