Artificial Intelligence Engineer

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The AI Engineer will drive implementation of artificial intelligence and machine learning solutions to enhance semiconductor back-end manufacturing operations. The role will involve close collaboration with manufacturing teams, data scientists, product and process engineers to identify opportunities and implement AI-driven solutions that improve operational efficiency, quality, and yield.

This position requires a strong understanding of AI/ML technologies, experience with semiconductor manufacturing, and a passion for applying data-driven techniques to solve complex industrial problems.
 

Department: Smart Manufacturing Automation (SMA) 

Number of Opening:  3 AI Engineer 

• 1 Opening:   AI Engineer (Data Engr & Agentic AI Development)
• 2 Opening:  AI Engineer (Command Center & Lights Out)

Intake:  Immediate

Work Arrangement:  (Onsite Working Arrangement at Kuala Lumpur, Malaysia)

Responsibilities include:

• Collaborate with cross-functional teams and domain experts to identify opportunities for AI integration in workflows.
• Analyze and preprocess large datasets from manufacturing equipment, sensors, and logs to enable effective machine learning applications.
• Lead proof-of-concept projects and pilot programs to demonstrate the value of AI applications in manufacturing settings.
• Develop and deploy AI/ML models for defect detection, yield improvement, process optimization, and predictive maintenance in semiconductor back-end manufacturing processes.
• Provide technical documentation, training, and support to ensure seamless adoption of AI solutions by production teams.
• Monitor AI systems performance and implement enhancement.
• Stay updated with the latest advancements in AI/ML and propose innovative solutions applicable to semiconductor manufacturing.
• Set up and manage AI development and production infrastructure.
• Help AI product managers and business stakeholders understand the potential and limitations of AI when planning new products.
• Build data ingest and data transformation infrastructure.
• Identify transfer learning opportunities and new training datasets.
• Build AI models from scratch and help product managers and stakeholders understand results.
• Deploy AI models into production.
• Create APIs and help business customers put results of your AI models into operations.
• Keep current of latest AI research relevant to our business domain.
• Collaborate with Data Analytics team to integrate and implement large scale data solution. 
• Participate in fast-paced prototyping to identify improvement opportunities in manufacturing systems.
   

Minimum requirements:

• Bachelor's degree in Computer Science, Data Science, Engineering or a related field.
• Strong programming skills in Python, R, VB.Net or equivalent languages.
• Proficiency in machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Experience in Data Analytics, AI vision or Gen-AI.
• Experience and Proficiency  in Full Stack Development.
• Experience in deploying machine learning models in production environments, including edge devices or cloud-based systems.
• Excellent analytical and problem-solving skills and ability to work collaboratively in a cross-functional team.
• Strong verbal and written communication skills.
   

Preferred qualifications:

• Knowledge of advanced computer vision techniques, such as deep learning for image classification, segmentation and object detection.
• Knowledge of LLM, Prompt Engineering, RAG and LLM fine-tuning.
• Experience with AI model explainability, trustworthiness, and validation in industrial contexts.
•  Knowledge of semiconductor back-end manufacturing processes, including assembly, packaging, and testing.
• Experience in working with semiconductor manufacturing data, including sensor data, equipment logs, and MES data.
• Familiarity with industry standards and protocols (e.g., SECS/GEM standards for semiconductor equipment).
• Familiarity with data analysis tools and techniques.
• Proven track record of publications, patents, or conference presentations in AI/ML applications for manufacturing
   

Added Advantage:

• Knowledge of semiconductor manufacturing process or equipment. 
• Knowledge in sensors, drivers and encoder will be an added advantage.
• Familiarity with Data Processing Tools (Kafka, Spark, Apache Nifi, Dataiku). 
• Familiarity with No-SQL databases (Cassandra, Mongo, HDFS). 
• Familiarity with software engineering tools (JIRA, Jenkins, Git, Confluence). 
• Excellent knowledge of algorithms and data structures.