Staff/Senior IoT Engineer, Package Health - APTD

Position Summary 
As an IoT Engineer, Engineer based in Singapore, you will design, build, and deploy Industrial IoT (IIoT) and machine vision solutions to support advanced packaging technology development and factory enablement. You will implement hands-on engineering work from proof-of-concept through deployment and sustainment, contributing to scalable and secure solutions that improve quality, productivity, and early risk detection. You will collaborate cross-functionally to translate manufacturing needs into measurable signals, robust data pipelines, and actionable insights, while operating with a strong safety mentality and a bias for practical, reliable delivery. 

Key Responsibilities 

• Develop and deploy IIoT solutions end-to-end: sensing strategy, edge compute, connectivity, data ingestion/storage, monitoring, and sustainment (including data quality and traceability). 

• Build machine vision solutions: support hardware selection (cameras/optics/lighting), image acquisition strategy, algorithm development, performance validation, and deployment readiness. 

• Work with process, equipment, automation, quality, and IT/data platform team members to translate manufacturing problems into measurable signals and integrated factory solutions. 

• Implement principled engineering practices: requirements gather, design reviews, coding standards, testing, documentation, change control, and cybersecurity considerations. 

• Solve issues in lab and factory environments; perform root-cause analysis, implement corrective actions, and improve system reliability and observability. 

• Contribute to technical planning and delivery: provide effort estimates, handle tasks, communicate status/risks, and support safe execution and timely closure of actions. 

​Working Relationships 

• Collaborate with operations, maintenance, and equipment suppliers to enable safe deployment and stable operation of IIoT/machine vision solutions with clear critical issue paths. 

• Partner with process and integration teams to refine requirements, implement development plans, and drive technology readiness through pilot and scale-up. 

• Work with metrology/inspection and quality partners to define measurement strategy, validate performance, and support response plans. 

• Coordinate with distributed team members to align standards, share findings, and help scale guidelines across sites. 

AI Responsibilities

• Integrates AI-assisted tools and insights into daily work to improve efficiency, quality, or effectiveness, exercising sound judgment and complying with organizational standards and legal requirements.

• Contributes to a culture of continuous improvement by identifying, testing, and sharing AI-enabled enhancements within one’s scope of work.

Qualifications 

• Bachelor’s degree (or higher) in Electrical/Electronics Engineering, Computer Engineering, Computer Science, Mechatronics, or related discipline; or equivalent practical experience. 

• Hands‑on experience delivering IoT/IIoT, automation, or machine vision solutions in industrial environments, with exposure to system integration and troubleshooting. Solid understanding of IoT architectures, including edge computing, connectivity, data ingestion, and device/data security fundamentals. 

• Proficiency in Python and familiarity with software engineering practices (code reviews, testing, CI/CD, and documentation). 

• Strong analytical/problem-solving skills and clear communication in a cross-cultural environment. 

• Willingness to work in lab/factory settings and follow safety, change-control, and operational procedures. 

• Machine vision hardware/software experience (camera selection, lenses, lighting, calibration) and algorithm development (OpenCV or similar). 

• Experience translating manufacturing/tool constraints into measurable signals and sensing strategies (e.g., camera, vibration, ESD/charge, IMU) to detect abnormal conditions and risk. 

• Data engineering fundamentals: building reliable pipelines, data quality checks, traceability, monitoring/alerting, and scalable data access patterns. 

• Embedded/edge prototyping and integration (e.g., Arduino, Raspberry Pi) and fixing networks/connectivity in factory environments. 

• Working knowledge of basic project execution: breaking down work, handling dependencies, and communicating progress and risks.