Jobfoundry
Jobfoundry
A*STAR

Senior Engineer, Project Management, ARTC

A*STAR
ResearchSingaporeOnsitePosted 2 weeks ago
Apply Now

About the role

AI summarised

Lead end-to-end AI deployment projects in industry settings, bridging the gap between advanced research and practical manufacturing application. This role requires orchestrating cross-functional teams to translate operational pain points into successful, governed deployments while measuring tangible business impact.

ResearchOnsite

Key Responsibilities

  • Plan, execute, monitor, and close concurrent AI deployment projects across manufacturing partners, ensuring delivery on scope, schedule, cost, quality, and readiness.
  • Deep-dive with technical/research teams and industry stakeholders to convert operational pain points into clear requirements, milestones, and acceptance criteria.
  • Act as the execution integrator across AI researchers, domain experts, engineering teams, and business units to ensure smooth delivery.
  • Ensure compliance with programme governance and grantor requirements while driving disciplined reporting to internal and external stakeholders.
  • Proactively manage delivery risks (e.g., data readiness, integration constraints, scope creep) and execute change control to protect project outcomes.
  • Define and track project outcome indicators (e.g., time savings, productivity) and package evidence for programme impact reporting.

Requirements

  • Bachelor's degree in Engineering, Industrial Systems, or Manufacturing-related discipline.
  • 5+ years of project management experience delivering technology projects in manufacturing/production environments (e.g., automation, Industry 4.0).
  • Demonstrated experience delivering cross-functional, multi-party projects involving AI/analytics or industrial software deployments.
  • Strong project execution discipline including scope/schedule/cost control, risk management, and governance compliance.
  • Strong stakeholder management and communication skills to interface with industry leaders and internal teams.
  • High adaptability and structured problem-solving ability to navigate R&D-to-deployment ambiguity.
  • Working understanding of manufacturing operations and production technologies (e.g., MES/ERP, shopfloor data, process flows).