We are partnering with a leading research and innovation organisation to hire a Software Developer to support the development of scalable, high-availability platforms for autonomous manufacturing and advanced planning systems.
This role focuses on building microservices-based applications that enable intelligent production planning, scheduling, and enterprise automation solutions. You will work in a collaborative environment combining applied research and real-world industry implementation.
Key Responsibilities:
- Design, develop, test, and maintain enterprise planning and scheduling applications (e.g., production planning, operations scheduling, inventory management).
- Build scalable microservices-based solutions using modern development frameworks.
- Analyse complex planning and scheduling challenges and implement effective software solutions.
- Collaborate with developers, researchers, and cross-functional stakeholders to deliver high-quality software.
- Deploy applications into production environments and provide ongoing maintenance and support.
- Identify performance bottlenecks and optimise application performance and scalability.
- Explore and integrate Generative AI capabilities to enhance planning and scheduling functionalities.
- Document system architecture, technical specifications, and user manuals.
Requirements
- Bachelor's or Master's degree in Computer Science, Engineering, or related technical discipline.
- Experience in software development, ideally within enterprise or complex system environments.
- Strong technical skills in: C# .NET , MS SQL and Angular
- Experience with microservices architecture.
- Understanding of CI/CD pipelines and DevSecOps practices.
- Strong analytical, troubleshooting, and problem-solving skills.
- Strong communication skills and ability to work with multiple stakeholders.
- Exposure to Generative AI / Agentic AI applications.
- Experience with manufacturing planning, scheduling, inventory, or supply chain systems.
- Knowledge of Siemens APS or similar advanced planning platforms.
- PhD candidates with optimisation (operations research) or machine learning expertise are welcome.
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