Manufacturing Engineer – Vision Systems (CBS)

Amphenol TCS de México, S.A. de C.V.; Mexicali; Presencial; MXN 480.000 - 600.000; Jornada completa

Descripción de la vacante

Amphenol TCS de México, S.A. de C.V. is seeking a Manufacturing Engineer – Vision Systems to partner with NPI, Quality, Automation, and Operations.

You will own inspection processes, select cameras and lighting, define inspection recipes, and drive robust AI-enabled inspection from transfer to mass production. The role emphasizes design-for-inspection, process validation, and hands-on support across EVT to ramp, with strong emphasis on defect reduction and continual improvement in a fast-paced

Formación

• Bachelor's degree in Electrical, Electronics, Mechatronics, Automation, Computer, Manufacturing, Mechanical, or Industrial Engineering, or a related technical discipline.
• Minimum of 4 years of hands‑on manufacturing or process engineering experience with machine vision, AOI, automated inspection, or AI vision systems in electronics, connectors, cable assemblies, semiconductor, automotive, or medical devices.
• Proven experience developing, qualifying, transferring, optimizing, and troubleshooting vision or AOI inspection processes from NPI through stable production.
• Working knowledge of industrial cameras, optics, lens selection, lighting techniques, image processing, OCR/OCV, pattern matching, dimensional inspection, defect classification, deep learning, anomaly detection, and integration with PLCs, robots, MES, and traceability platforms.
• Strong knowledge of PFMEA, control plans, process validation, SPC, attribute MSA, Gauge R&R, DOE, golden-sample control, recipe and model version control, calibration, and engineering change control.
• Proven problem‑solving capability using 8D, 5‑Why, Fishbone, Pareto analysis, DOE, and CAPA.
• Experience with Python, OpenCV, Cognex, Keyence, Omron, HALCON, or similar deep‑learning vision platforms is preferred.
• Advanced knowledge of Microsoft Office Suite; vision programming software, SQL, Power BI, and MES are a plus.
• Results‑oriented with a focus on inspection accuracy, false‑call reduction, and prevention of customer escapes.
• Hands‑on technical capability, project execution skills, and the ability to support production priorities in a fast‑paced environment.
• Advanced English and excellent communication skills in Spanish and English.

Responsabilidades

• Own manufacturing readiness and ongoing performance for assigned inspection processes, including camera and lens selection, lighting, optics, image acquisition, fixturing, algorithms, defect libraries, acceptance criteria, equipment integration, validation, and documentation.
• Define, develop, validate, release, and maintain inspection recipes, camera positions, field of view, resolution, lighting methods, image-processing parameters, defect classifications, and thresholds for connectors, cable routing, terminals, welds, labels, shielding, and assembly presence.
• Lead design-for-inspection reviews focused on feature visibility, contrast, access, orientation, tolerance definition, defect detectability, and fixture repeatability.
• Specify, qualify, install, and support cameras, lenses, lighting, vision controllers, motion systems, robotics, fixtures, reject mechanisms, and poka-yoke devices; coordinate suppliers, FAT/SAT, calibration, and preventive maintenance.
• Develop DOE and validation plans to confirm detection capability, repeatability, reproducibility, false reject and false accept rates, and process capability.
• Provide hands‑on engineering support during EVT, DVT, PVT, pilot, ramp, and mass production builds, including system setup, recipe development, image tuning, false‑call reduction, and rapid corrective‑action closure.
• Lead investigation and correction of missed defects, false calls, image instability, lighting variation, fixture misalignment, software faults, and equipment downtime using 8D, 5‑Why, Fishbone, DOE, and statistical methods.

Conocimientos

Machine vision

AOI

Image processing

Python

OpenCV

Cognex

Keyence

Omron

HALCON

SQL

Power BI

MES

Educación

Bachelor's degree in Electrical/Electronics/Mechatronics/Automation/Computer/Manufacturing/Mechanical/Industrial Engineering or related technical discipline

Herramientas

Cognex

Keyence

Omron

HALCON

Descripción del empleo

The Manufacturing Engineer – Vision Systems is responsible for working in collaboration with NPI, Quality, Automation, and Operations teams to lead process ownership for machine vision, automated optical inspection (AOI), and AI-enabled inspection systems used in high-speed cable assemblies and backplane systems — supporting NPI, process transfer, production ramp, and stable mass production.

Responsibilities:

• Own manufacturing readiness and ongoing performance for assigned inspection processes, including camera and lens selection, lighting, optics, image acquisition, fixturing, algorithms, defect libraries, acceptance criteria, equipment integration, validation, and documentation.
• Define, develop, validate, release, and maintain inspection recipes, camera positions, field of view, resolution, lighting methods, image-processing parameters, defect classifications, and thresholds for connectors, cable routing, terminals, welds, labels, shielding, and assembly presence.
• Lead design-for-inspection reviews focused on feature visibility, contrast, access, orientation, tolerance definition, defect detectability, and fixture repeatability.
• Specify, qualify, install, and support cameras, lenses, lighting, vision controllers, motion systems, robotics, fixtures, reject mechanisms, and poka-yoke devices; coordinate suppliers, FAT/SAT, calibration, and preventive maintenance.
• Develop DOE and validation plans to confirm detection capability, repeatability, reproducibility, false reject and false accept rates, and process capability.
• Provide hands‑on engineering support during EVT, DVT, PVT, pilot, ramp, and mass production builds, including system setup, recipe development, image tuning, false‑call reduction, and rapid corrective‑action closure.
• Lead investigation and correction of missed defects, false calls, image instability, lighting variation, fixture misalignment, software faults, and equipment downtime using 8D, 5‑Why, Fishbone, DOE, and statistical methods.

• Bachelor's degree in Electrical, Electronics, Mechatronics, Automation, Computer, Manufacturing, Mechanical, or Industrial Engineering, or a related technical discipline.
• Minimum of 4 years of hands‑on manufacturing or process engineering experience with machine vision, AOI, automated inspection, or AI vision systems in electronics, connectors, cable assemblies, semiconductor, automotive, or medical devices.
• Proven experience developing, qualifying, transferring, optimizing, and troubleshooting vision or AOI inspection processes from NPI through stable production.
• Working knowledge of industrial cameras, optics, lens selection, lighting techniques, image processing, OCR/OCV, pattern matching, dimensional inspection, defect classification, deep learning, anomaly detection, and integration with PLCs, robots, MES, and traceability platforms.
• Strong knowledge of PFMEA, control plans, process validation, SPC, attribute MSA, Gauge R&R, DOE, golden-sample control, recipe and model version control, calibration, and engineering change control.
• Proven problem‑solving capability using 8D, 5‑Why, Fishbone, Pareto analysis, DOE, and CAPA.
• Experience with Python, OpenCV, Cognex, Keyence, Omron, HALCON, or similar deep‑learning vision platforms is preferred.
• Advanced knowledge of Microsoft Office Suite; vision programming software, SQL, Power BI, and MES are a plus.
• Results‑oriented with a focus on inspection accuracy, false‑call reduction, and prevention of customer escapes.
• Hands‑on technical capability, project execution skills, and the ability to support production priorities in a fast‑paced environment.
• Advanced English and excellent communication skills in Spanish and English.