Vision System Engineer

Bosch Group•Lincolnton, NC

3h•Onsite

About The Position

The Vision System Engineer is responsible for the implementation, tuning, and industrialization of Automated Optical Inspection (AOI) solutions enhanced with AI in the shopfloor. The role focuses on replacing manual inspection activities to reduce direct headcount, improve inspection robustness, and reduce scrap and rework. Positioned within TEF2, the role acts as a technical expert for vision-based systems, leading POCs, configuring AOI solutions, and validating AI-based classification models in production environments, while aligning with TEF1 project governance for industrial execution.

Requirements

Bachelor’s Degree in Electrical Engineering, Mechatronics Engineering, Automation Engineering, Computer Engineering, or related field
Automated Optical Inspection (AOI) systems configuration and deployment in manufacturing environments Senior
Machine vision technologies (cameras, lighting, optics, image acquisition principles) Senior
AI-based vision inspection (classification, pattern recognition, OK/NOK decision logic) Advanced
Model training, tuning, validation, and performance optimization for vision-based AI applications Advanced
Data preparation and labeling for AI vision use cases Advanced
Understanding of manufacturing processes and inspection points in shopfloor environments Intermediate
Proof of Concept (POC) execution and technical feasibility assessment Advanced
Technical coordination with AOI / AI vendors and system integrators Advanced
Industrialization support and commissioning of vision systems in production Advanced
Root cause analysis related to inspection failures, false rejects, and escapes Advanced
Application of standard solutions and reuse-oriented implementation mindset Advanced
Documentation of technical configurations, models, and lessons learned Intermediate
Communication skills to explain technical solutions to non-technical stakeholders Intermediate

Nice To Haves

Post-Graduate Degree Artificial Intelligence, Computer Vision, Robotics, Data Engineering Special License/Certification Machine Vision (Cognex, Keyence, Omron, etc.), AI / Vision training or certification (desirable)

Responsibilities

Implements and configures Automated Optical Inspection (AOI) systems in shopfloor environments.
Tunes vision parameters and AI-based classification models to achieve robust OK/NOK decisions under high process variability.
Ensures stable and repeatable inspection performance in production conditions.
Trains, validates, and optimizes AI vision models to replace manual inspection tasks and reduce direct headcount.
Performs data preparation, model tuning, and performance validation in alignment with defined acceptance criteria.
Develops custom AI algorithms from scratch only in exceptional, approved cases with clear business impact.
Leads technical Proof of Concepts (POCs) for AOI and AI-based inspection solutions within TEF2 scope.
Evaluates technical feasibility, robustness, and scalability of vision solutions prior to project industrialization.
Provides technical recommendations to support go / no-go decisions for AOI projects.
Supports the industrialization of AOI solutions once released into the TEF1 project portfolio.
Works closely with TEF1 Project Leaders to ensure technical readiness and smooth deployment.
Provides technical input for commissioning, validation, and handover to production.
Acts as the main technical interface with AOI and AI solution providers, system integrators, and central teams (BD, BDO, etc.).
Supports supplier evaluations, testing, and solution optimization without assuming commercial or contractual responsibilities.
Ensures delivered solutions meet technical and performance expectations.
Tracks KPIs related to direct headcount reduction, scrap reduction, and inspection effectiveness.
Identifies improvement opportunities to enhance inspection accuracy and reduce false rejects or escapes.
Drives continuous optimization of deployed AOI and AI solutions.
Applies standard AOI and AI solutions aligned with central and TEF2 guidelines.
Documents configurations, models, and lessons learned to enable reuse and maintainability.
Ensures technical documentation supports long-term sustainability of solutions