全职 Engineer, Data Analytics Engineering (AI-ML) 工作, 薪水, Sandisk WP Kuala Lumpur 公司招聘中 - Ricebowl

We are looking for a curious and motivated Junior Data Management Engineer with foundational experience in machine learning, Computer Vision and GenAI  to join our growing data team. This role is perfect for someone early in their career who is eager to contribute to data engineering tasks while gaining hands-on exposure to machine learning workflows. You'll help ensure data pipelines are optimized for analytics and ML model development, supporting both operational and strategic decision-making. 

ESSENTIAL DUTIES AND RESPONSIBILITIES:

• Work with multiple stakeholders to identify, diagnose and resolve manufacturing process related problems such as quality excursions, manufacturing excellence and cost improvement. 
• Apply computer vision algorithms to inspect production lines for defects, improve yield, and enhance overall quality control. Leverage techniques such as object detection, image segmentation, and defect localization to identify issues early in the manufacturing process. 
• Build, train, and validate machine learning models to address complex manufacturing challenges, such as defect detection, process optimization, and performance prediction. 
• Able  to deploy trained models into production environments, including APIs, edge devices, or embedded systems. Ensure seamless integration with manufacturing systems and establish real-time monitoring for model performance. 
• Design and develop scalable GenAI applications using LLMs tailored to manufacturing use cases.

REQUIRED:

• Bachelor’s degree in Computer Science, Data Science, Engineering, Statistics, or a related field. 
• 0–2 years of experience in data analytics, data engineering, or machine learning. 
• Proficiency in SQL for data querying and manipulation. 
• Strong programming skills in Python; experience with SQL, Spark, or other big data tools for processing large-scale manufacturing/sensor datasets. 
• Proficiency in ML frameworks (PyTorch, TensorFlow/Keras) and CV libraries (OpenCV, Detectron2, MMDetection). 
• Experience with model deployment in production (eg, Docker, Kubernetes, edge devices) for real-time or batch inference.

PREFERRED:

• Exposure to building or assisting in ML models using libraries like scikit-learn or TensorFlow. 
• Experience with data visualization tools (e.g., Spotfire, Power BI, Tableau). 
• Familiarity with version control tools such as Git. 
• Exposure to ML lifecycle tools like MLflow, DVC, or Kubeflow. 

SKILLS:

• Data preprocessing and feature engineering techniques. 
• Ability to interpret and visualize data insights. 
• Strong analytical and problem-solving mindset. 
• Effective written and verbal communication. 
• Team collaboration and a proactive learning attitude.