Engineering Digital Operations Engineer (AI 應用工程師) #194753 (淡水或是其他辦公室)

Your Job
We are seeking a hands-on AI Automation Engineer to design, build, integrate, deploy, and continuously improve production-ready automation solutions for NPI Product Design and Engineering Operations. The role combines strong Python development, practical AI/LLM application engineering, and API architecture to connect engineering data, documents, workflows, and enterprise systems.

About the Role:
This is a full-time engineering role reporting to the Engineering Digital Operating System Owner. You will translate approved business and engineering requirements into reliable software services, AI-assisted workflows, data pipelines, and system integrations supporting Taiwan NPI PD, the India Support Team, Vietnam PD, Architecture, Manufacturing, Quality, and IT. You will be expected to own implementation from prototype through testing, deployment, monitoring, documentation, and operational support, while partnering with process owners on architecture, governance, security, and release decisions.

Our Team
Molex creates connections for life by enabling technologies that transform the future and improve lives. Our NPI Product Design / Engineering Operations team is building a low-burden, traceable, measurable, and continuously improving way of working. We partner across Taiwan, India, Vietnam, manufacturing, quality, production, factories, and IT to reduce manual tracking, duplicate entry, waiting, rework, and Drawing/BOM/Release errors while improving engineering delivery and knowledge reuse.

What You Will Do (Job Description)

1. Build AI-Enabled Engineering Automations:
Develop maintainable Python applications, scripts, and services that automate document handling, data preparation, classification, summarization, validation, reporting, notifications, and engineering workflow activities.
2. Design APIs and System Integrations:
Design, build, consume, test, and document REST APIs and integration services. Implement authentication, authorization, validation, exception handling, retries, logging, versioning, and secure exchange of JSON-based data across engineering and enterprise systems.
3. Manage Data and Production Deployment:
Design fit-for-purpose data structures and database interactions; deploy solutions on an approved cloud platform; use source control, code review, configuration management, testing, monitoring, and release practices to make automations reliable, traceable, and maintainable.
4. Integrate Microsoft 365 and Low-Code Platforms:
Where appropriate, connect Python and AI services with Power Automate, Microsoft Graph, Copilot Studio, SharePoint, Microsoft Teams, and Power Apps to deliver usable end-to-end workflows without forcing low-code tools to perform tasks better suited to software services.
5. Validate, Operate, and Improve Solutions:
Translate ambiguous operational problems into testable requirements, compare solution options, run pilots and shadow tests, measure accuracy and operational impact, troubleshoot failures, document support procedures, and improve solutions based on evidence and user feedback.