Job Description
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Description
- Architect and develop the core systems that runs on physical manufacturing test stations, seamlessly integrating AI models, UI/UX for factory operators, and hardware control logic. - Deploy and optimize machine learning models and computer vision algorithms directly onto edge devices (e.g., Mac minis, industrial PCs) to automate visual inspection, fault detection, and functional testing. - Develop robust systems to control and communicate with physical test fixtures, sensors, PLCs, and industrial automation equipment using standard protocols (RS232, UART, TCP/IP). - Lead the rollout and scaling of these intelligent test systems across regional factories, ensuring smooth integration with the existing factory network and Manufacturing Execution Systems (MES) and Data collection systems. - Act as the technical lead during New Product Introductions, debugging complex integration issues between the AI Tools, the edge hardware, and the physical test fixtures during the ramp-up phase. - Utilize data analytics and AI-driven insights from the test stations to optimize test sequences, reduce cycle times, and identify root causes of manufacturing defects. - Partner with global AI development teams, hardware engineers, and regional factory operations to translate complex test requirements into robust, deployable solutions.
Minimum Qualifications
- BS or MS in Software Engineering is required.
- Minimum 4 years of relevant work experience in manufacturing software development, factory automation, or industrial IoT systems.
- Adept at utilizing GenAI platforms to drive process improvements, analyze data, and accelerate project delivery
- Strong proficiency in Python (specifically for ML/AI integration), Embedded C/C++, Perl, Ruby, and Shell Scripting (Bash).
- Proficient in leveraging Generative AI coding assistants (e.g., GitHub Copilot, Cursor) to accelerate development, refactor code, and streamline debugging used for hardware control, automation, and AI model integration.
- Excellent working knowledge of industrial communication and networking protocols (TCP/IP, MQTT, RS232, UART).
- Hands-on experience deploying machine learning models, specifically computer vision/inspection models, to edge hardware in real-time environments.
- Proven experience developing systems that interfaces directly with physical hardware, sensors, test instruments, or robotics.
- Exceptional ability to debug complex, multi-disciplinary systems (where systems, networking, and physical mechanics intersect) on a live manufacturing line.
Preferred Qualifications
- Experience working across global manufacturing sites, including direct floor-level engagement with production lines
- Self-motivated with an entrepreneurial spirit, excellent time management, and the strong written/verbal communication skills necessary to explain complex technical trade-offs.
- Experience in performance management and team development, fostering a culture that embraces new technologies and AI adoption.
