Determining the core aspects and success criteria for automation solutions in the area of packaging. Responsible for the technical leadership of one or more engineers in the same thematic field according to the Corporate Rules of Governance.
Responsible to develop User’s Requirement Specification (URS) and Functional Specification (FS)
Defining and accomplishing projects in the area of production process automation for packaging solutions in co-operation with internal and external suppliers.
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Interface silicon, power architect, firmware and platform team to close power management feature evaluation in pre-silicon phase
Develop and execute feature enablement and validation test plans for power management features during emulation and post silicon phase across all Server GPU products
Work closely with HOD to set up an organizational structure within BMI addressing core manufacturing processes and the consequences for related technical training.
Responsible for disciplinary and technical leadership of the area according to the Corporate Rules of Governance. Identifying, defining and accomplishing projects in the area of core production processes in co-operation with internal and external suppliers.
Responsible for guiding the nominated technical experts from the own organization in the global CoC and the related sub-CoC groups (Center of Competence). Develop suitable strategies to execute governance over the core processes within the area of regional responsibility defined for the BTC-AP BMI in relation to the OMM (Operating Model Medical).
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Job Title : Data Automation Engineer (AI & Computer Vision) – Contract
Job Summary: We are looking for a specialized Data Automation Engineer under SIP process engineering department to spearhead advanced defect analysis projects. You will be responsible for developing and deploying real-time automation systems that utilize AI/ML and Computer Vision to identify production defects and manage Quality Discrepancy Notification (QDN) lot containment. This is a critical role aimed at achieving "zero-defect" manufacturing through intelligent, hands-off data orchestration.