Simulation Engineer - Industrial AI Solutions

Roles & Responsibilities : Model Development & Validation: Develop, train, and validate machine learning models (regression, classification, time series, etc. ) based on project requirements and defined hypotheses. Build and calibrate simulation models of industrial processes, equipment, and systems. Utilize data analytics techniques to extract insights from large datasets and identify opportunities for optimization. Integrate domain knowledge and physics-based principles into data-driven models to improve accuracy and robustness. Conduct statistical analysis, develop visualizations, and communicate with stakeholders. Develop and maintain documentation of models, algorithms, and code. Stay up-to-date on the latest advances in AI, ML, and simulation techniques. Explore new technologies and approaches to solve industrial problems. ML Engineering & MLOps: Implement MLOps best practices for model deployment, monitoring, and maintenance. Automate model training, validation, and deployment pipelines. Work with IT and DevOps teams to deploy models into production environments. Monitor model performance and retrain models as needed to maintain accuracy. Edge Analytics & Deployment: Develop and deploy lightweight AI models for edge computing environments. Design and implement data pipelines for edge data collection and processing. Work with hardware engineers to integrate models with edge systems. Collaboration & Communication: Collaborate with domain experts, program managers, and other data analysts / engineers to understand business problems and develop solutions. Communicate complex technical concepts to non-technical audiences. Participate in code reviews and knowledge sharing sessions. Contribute to the development of best practices and standards for data science and AI.