Total 3 process development engineer job vacancies in Information Technology category in The Philippines

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.

The Automation & Analytics team is seeking a highly motivated Software & AI Platform Engineer to help drive the next generation of intelligent factory solutions. In this role, you will contribute to the design, development, and evolution of in-house digital platforms that power automation, analytics, and AI-driven operations across semiconductor manufacturing environments.

Beyond traditional software and data engineering, this role focuses on enabling AI capabilities within operational platforms, including intelligent agents, real-time analytics, workflow automation, computer vision, and AI-assisted decision support. You will work closely with cross-functional engineering, manufacturing, automation, and operations teams to build scalable systems that support the Factory of the Future vision.

This position is ideal for individuals passionate about software engineering, AI systems, industrial digitalization, and building impactful technologies that bridge manufacturing operations with advanced analytics and generative AI.

ESSENTIAL DUTIES AND RESPONSIBILITIES: 

• Design, develop, and maintain scalable software platforms supporting factory automation, analytics, and AI applications.
• Build and optimize data pipelines, APIs, and backend services for structured and unstructured manufacturing data.
• Contribute to the integration of AI capabilities into operational platforms, including machine learning, generative AI, intelligent agents, and workflow automation.
• Collaborate with cross-functional stakeholders including engineering, manufacturing, automation, IT, and operations teams to deliver end-to-end digital solutions.
• Develop solutions that support real-time data processing, event-driven architectures, and streaming analytics environments.
• Participate in the development of AI-enabled applications such as conversational assistants, recommendation systems, anomaly detection, computer vision, or predictive analytics solutions.
• Ensure system reliability, scalability, data quality, and security through monitoring, testing, and documentation.
• Support platform interoperability through APIs, microservices, and integration frameworks.
• Contribute to software architecture discussions, technical roadmaps, and continuous improvement initiatives.
• Stay current with emerging technologies in AI, cloud-native systems, industrial automation, and software engineering best practices.

ESSENTIAL DUTIES AND RESPONSIBILITIES: 

• Perform data mining and analytics for Quality Assurance (QA) in semiconductor Assembly & Testing processes
• Understand QA business workflows (e.g., SPC, 8D, defect management) and translate business needs into scalable data solutions
• Design and develop end-to-end data pipelines, including data ingestion, transformation, modeling, and visualization
• Develop analytical methods such as anomaly detection, trend analysis, and predictive modeling to enhance quality control
• Drive the transformation of quality management from manual processes to automated and intelligent systems
• Develop dashboards, reports, and automated QA-related business applications
• Collaborate with IT, data teams, and business stakeholders to integrate data platforms with operational systems
• Explore and implement AI/GenAI use cases in quality engineering, such as intelligent insights and automated reporting