Estudiante de Manufactura en Mantenimiento
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Responsibilities
- Planning and monitoring major preventive maintenance activities.
- Weekly meetings with suppliers of predictive sensors.
- Developing presentations for courses implemented as part of the TPM project.
- The student will be trained in:
- Lockout/Tagout (LOTO)
- Administrative Skills - Percipio
- Self Development - Percipio
- Self Discovery - Percipio
- Gemini for Workspace - Percipio
- Power BI - Percipio
- The requirements are as follows:
- Availability for 1 year as an active student (enrolled in university through June-July 2027).
- Availability to work 7 morning hours Monday through Friday, preferably from 8:00 AM to 3:00 PM.
- Student majoring in Industrial and Systems Engineering, Industrial Engineering, or Electrical and Mechanical Engineering
- Must have a Federal Taxpayer ID (RFC).
- English proficiency: Intermediate (speaking, writing, and listening).
- Positive attitude and a strong desire to learn.
Benefits
Additional Information
Manufacturing Intern Location: Blvd Nexxus AND# 2505 Cienega De Flores, 65550 Mexico, N.L., Mexico Make sure your first professional steps are with a global and inclusive company. We are actively seeking students interested in developing high-impact projects while pursuing their university degree. At Cummins, you'll work alongside highly innovative thinkers and technology leaders in a diverse and inclusive environment to make meaningful contributions to the business, our customers, our communities, and the world. You'll have all the resources and support you need from our teams and leaders to advance your professional career, as well as to achieve an optimal work-life balance by aligning your individual needs and aspirations. Specific information about the area: The Maintenance department is responsible for ensuring the availability and reliability of equipment through preventive, corrective, and predictive maintenance activities. The project to implement the TPM (Total Productive Maintenance) methodology aims to increase equipment efficiency by eliminating major maintenance-related losses. The project focuses on predictive maintenance, that is, the timely detection of potential failures before they cause unplanned machine downtime.
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