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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 (2025)

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.
  

Minimum requirements:

• Masters in Electrical Engineering, Mechanical Engineering or related engineering degree
• XX years of experience with project engineering/management experience on highly complex products/projects
• Proven leadership capabilities in leading multifaceted global organizations
• Successful proven track record in indirect management and influence

Preferred qualifications:

• Ability to manage multiple projects simultaneously, and set realistic plans based on required tasks with available resources
• Ability to effectively coordinate and lead global development teams on projects that include design, silicon fabrications, production test development, qualification and production ramp; Programs will be driven out of multiple global locations
• Ability to provide clear direction to both internal teams and external support organizations like device fabrication and assembly sites
• Knowledge of device packaging and wafer fabrication process
• Knowledge of reliability requirements and tri-temp characterization methodology using statistical analysis
• Ability to work in a team environment and understand cross-functional team dynamics
• Ability to work with global customers to align project schedules with customers' key milestones
• Demonstrated strong analytical and problem solving skills
• Strong verbal and written communication skills
• Ability to work in teams and collaborate effectively with people in different functions
• Strong time management skills that enable on-time project delivery
• Demonstrated ability to build strong, influential relationships
• Ability to work effectively in a fast-paced and rapidly changing environment
• Ability to take the initiative and drive for results