AI Applications IT Specialist/ R&D and Manufacturing Digitalisation Engineer

Salary: $27,000 – $40,500 per month

About the role

Build practical AI and data capabilities that accelerate materials R&D, improve process predictability and make manufacturing intelligence accessible to technical teams. This role connects laboratory data, plant data and modern AI tools to deliver measurable operational value.

Key responsibilities

• Introduce and enable the effective use of AI tools—such as large language models, AI agents and materials-informatics platforms—so R&D teams can rapidly search global literature and patents and support formulation design for photocatalytic and other new products.

• Create data-capture pipelines and machine-learning models for titanium dioxide post-processing and fibre masterbatch lines to predict and optimise critical parameters, including masterbatch pressure filter value and final milling/classification performance.

• Develop AI-enabled early-warning models from front-line production history to identify emerging trends such as strand breakage, powder agglomeration and equipment wear, supporting predictive maintenance.

• Integrate and restructure laboratory R&D data and production-process data to remove information silos and establish a proprietary titanium dioxide and masterbatch knowledge base.

• Deliver practical AI training to powder and masterbatch engineers, including prompt engineering, automated data-processing scripts and other productivity tools.

About you

• Master's degree or above in Computer Science, Data Science, Materials Informatics/Chemoinformatics, Automation, or an interdisciplinary chemistry-and-computing field.

• 3+ years of applied IT support, data analytics or AI-delivery experience in industrial manufacturing, chemicals, materials or semiconductor environments.

• Strong Python or R capability and hands-on experience with mainstream machine-learning frameworks such as scikit-learn, TensorFlow or PyTorch.

• Applied generative-AI experience, including large-model APIs (e.g., OpenAI or Claude) or open-source models, and implementation of internal knowledge bases or RAG-enabled assistants.

• Industrial data integration using OPC UA, MQTT and SQL/NoSQL databases, with the ability to interface with PLC/DCS data.

• Ability to translate between production, R&D and digital teams with clear, practical communication.

• Working knowledge of chemical processing and powder-material manufacturing.

• Strong cross-disciplinary learning ability and comfort with specialist engineering terminology.