- Hong Kong Hong Kong
Johnson Electric Hiring! Full Time Senior Engineer – Injection Process AI - Data Application in Hong Kong - Ricebowl
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Job Description
Responsibilities
Job Purpose
The Senior Engineer will lead the development and deployment of AI-driven closed-loop injection process control to roll out our projects, focusing on plastic gear manufacturing and water pump component capability improvement. This role is accountable for establishing the end-to-end (E2E) injection process data application framework and educating the team to embed data-driven practices into daily work. Additionally, the role will upskill the team's technical competencies across modern AI/software tools, bridging the gap between manufacturing process knowledge and digital transformation.
Key Responsibilities
1. Injection Process AI & Closed-Loop Setup
Lead the design, development, and deployment of AI-based closed-loop process control for injection molding machines.
Drive the project rollout for plastic gear and water pump component application, ensuring measurable improvements in capability (Cp/Cpk) and defect reduction.
Integrate real-time process signals, sensor data, and quality feedback to build self-optimizing process loops.
Collaborate with tooling, quality, and production teams to validate AI models on production lines.
2. E2E Injection Process Data Application & JEB Enablement
Architect and implement an end-to-end data pipeline for injection molding process data — from machine/sensor acquisition through storage, analysis, visualization, and decision support.
Build dashboards and reporting tools to visualize process performance, OEE, capability trends, and AI model outputs.
Educate and mentor the JEB team (process engineers, technicians, operators) on how to interpret and act on data in their daily work — shifting from instinct-driven to data-driven decision making.
Create standard operating procedures (SOPs), training materials, and hands-on workshops for sustaining the data culture.
3. Team Capability Building (Software & AI Skills)
Drive technical upskilling of the engineering team across the following software and AI tool stack:
Python – for data analysis, scripting, automation, and ML model development.
SQL – for querying manufacturing databases, process historians, and MES systems.
LLM / RAG (Large Language Models & Retrieval-Augmented Generation) – for building knowledge assistants, troubleshooting guides, and document intelligence tools.
Azure AI Studio – for developing, deploying, and managing AI models in the cloud.
Power BI – for creating interactive process dashboards and real-time visual analytics.
Manufacturing Process Knowledge – deep understanding of injection molding, material science, and process optimization.
Lead at least 1–2 full-cycle AI project deployments into production, managing the end-to-end lifecycle from problem definition, data collection, model training, validation, deployment, and monitoring.
4. Cross-Functional Collaboration & Innovation
Partner with IT, Data Engineering, and OT (Operational Technology) teams to ensure seamless data connectivity between shop floor systems and cloud/analytics platforms.
Identify new opportunities for AI/ML application in manufacturing (e.g., predictive maintenance, defect classification, yield optimization).
Stay current with emerging trends in AI for manufacturing and introduce relevant innovations to the organization.
Document project outcomes, best practices, and lessons learned for organizational knowledge retention.
Required Qualifications & Skills
Education
Bachelor's degree or higher in Mechanical Engineering, Manufacturing Engineering, Computer Science, Data Science, Electrical Engineering, or a related technical field.
Experience
Hands-on experience with injection molding machines, process parameters, tooling, and material science (especially plastic gears and/or pump components).
Proven track record of deploying at least 1–2 AI/ML projects into production environments in a manufacturing context.
Experience with closed-loop process control systems is highly preferred.
Full-time
The Senior Engineer will lead the development and deployment of AI-driven closed-loop injection process control to roll out our projects, focusing on plastic gear manufacturing and water pump component capability improvement. This role is accountable for establishing the end-to-end (E2E) injection process data application framework and educating the team to embed data-driven practices into daily work. Additionally, the role will upskill the team's technical competencies across modern AI/software tools, bridging the gap between manufacturing process knowledge and digital transformation.
Key Responsibilities
1. Injection Process AI & Closed-Loop Setup
Lead the design, development, and deployment of AI-based closed-loop process control for injection molding machines.
Drive the project rollout for plastic gear and water pump component application, ensuring measurable improvements in capability (Cp/Cpk) and defect reduction.
Integrate real-time process signals, sensor data, and quality feedback to build self-optimizing process loops.
Collaborate with tooling, quality, and production teams to validate AI models on production lines.
2. E2E Injection Process Data Application & JEB Enablement
Architect and implement an end-to-end data pipeline for injection molding process data — from machine/sensor acquisition through storage, analysis, visualization, and decision support.
Build dashboards and reporting tools to visualize process performance, OEE, capability trends, and AI model outputs.
Educate and mentor the JEB team (process engineers, technicians, operators) on how to interpret and act on data in their daily work — shifting from instinct-driven to data-driven decision making.
Create standard operating procedures (SOPs), training materials, and hands-on workshops for sustaining the data culture.
3. Team Capability Building (Software & AI Skills)
Drive technical upskilling of the engineering team across the following software and AI tool stack:
Python – for data analysis, scripting, automation, and ML model development.
SQL – for querying manufacturing databases, process historians, and MES systems.
LLM / RAG (Large Language Models & Retrieval-Augmented Generation) – for building knowledge assistants, troubleshooting guides, and document intelligence tools.
Azure AI Studio – for developing, deploying, and managing AI models in the cloud.
Power BI – for creating interactive process dashboards and real-time visual analytics.
Manufacturing Process Knowledge – deep understanding of injection molding, material science, and process optimization.
Lead at least 1–2 full-cycle AI project deployments into production, managing the end-to-end lifecycle from problem definition, data collection, model training, validation, deployment, and monitoring.
4. Cross-Functional Collaboration & Innovation
Partner with IT, Data Engineering, and OT (Operational Technology) teams to ensure seamless data connectivity between shop floor systems and cloud/analytics platforms.
Identify new opportunities for AI/ML application in manufacturing (e.g., predictive maintenance, defect classification, yield optimization).
Stay current with emerging trends in AI for manufacturing and introduce relevant innovations to the organization.
Document project outcomes, best practices, and lessons learned for organizational knowledge retention.
Required Qualifications & Skills
Education
Bachelor's degree or higher in Mechanical Engineering, Manufacturing Engineering, Computer Science, Data Science, Electrical Engineering, or a related technical field.
Experience
Hands-on experience with injection molding machines, process parameters, tooling, and material science (especially plastic gears and/or pump components).
Proven track record of deploying at least 1–2 AI/ML projects into production environments in a manufacturing context.
Experience with closed-loop process control systems is highly preferred.
Full-time
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