About the role
AI summarisedDesign and build agentic AI-driven expert systems to enable predictive and autonomous decision-making within complex manufacturing environments. This role involves developing advanced AI agents, leveraging simulation/digital twins, and integrating predictive models to optimize production flows.
IDMOnsiteData Science
Key Responsibilities
- Develop agent-based and multiagent AI systems capable of planning, deciding, and acting on behalf of production assets.
- Design reasoning and decision policies utilizing rules, optimization, and machine learning to manage real-time manufacturing scenarios (e.g., dispatching, scheduling, quality control).
- Utilize simulation and digital twins to model production flows, equipment behavior, and system constraints for safe strategy testing.
- Integrate predictive models (e.g., machine health, yield) into expert systems to support proactive and prescriptive operational decisions.
- Evaluate AI solutions against KPIs including throughput, WIP, cycle time, OEE, and cost, driving iterative performance improvements.
- Document system architecture, algorithms, experiments, and support IP creation related to agentic AI systems.
Requirements
- Bachelor's or Master’s degree in Computer Science, AI, Data Science, Operations Research, or relevant Engineering field.
- Strong foundation in AI and Machine Learning principles.
- Exposure to Reinforcement Learning or Multiagent RL is required.
- Experience with planning and decision-making under uncertainty.
- Proficiency in Python and AI/ML ecosystems (e.g., PyTorch, TensorFlow, scikit-learn).
- Familiarity with simulation or modeling of manufacturing/logistics systems (e.g., digital twins).
- Strong analytical skills to formalize complex shopfloor logic into constraints and objectives.
- Ability to work with real operational data, including cleaning and feature engineering.