Valbruna Stainless Inc is looking to create end user and industrial data collection applications to satisfy the most sophisticated needs of the customer by updating its production capabilities and experience.
Requirements
- Applied knowledge of Python development (5+ years).
- Practical experience with AI/ML projects in a production or industrial setting.
- Must have a comprehensive understanding of database design and development.
- Experience with Ignition by Inductive Automation, including scripting, tag configuration, and HMI interface development.
- Exposure to DevOps practices, containerization, and automated deployments is desirable.
- Familiarity with OPC UA, MQTT, and industrial protocols is a plus.
- Experience with MES systems and HMI interfaces for real-time data visualization and operator interaction.
Responsibilities
- Develop and maintain applications written in Python.
- Design and implement AI and machine learning models to support predictive maintenance, process optimization, and non-conformance detection.
- Integrate AI/ML workflows into MES and HMI systems, using frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Develop and support applications using Inductive Automation’s Ignition platform, including Perspective and Vision modules.
- Build and maintain HMI/MES solutions, interfacing with PLCs and SCADA systems to collect, analyze, and visualize operational data.
- Collaborate with process engineers and data scientists to define data acquisition strategies and deploy ML insights to the shop floor.
- Write clean, maintainable code following best practices (unit testing, source control, continuous integration, automation, etc.).
Other
- Bachelor's degree in Computer Science, Computer Engineering, Industrial Automation, or related field.
- United States Citizenship or Permanent Residency (Required)
- Must be eligible to work in compliance with ITAR, EAR, and/or DFARS regulations
- Prior experience working in a manufacturing environment (5+ years)
- Demonstrate a collaborative mindset while working with globally dispersed and cross-functional teams.
