Development of a Scalable AI-Powered Predictive Maintenance System for Industrial Equipment using Real-Time Sensor Data

We are seeking an experienced AI/ML engineer to develop a comprehensive predictive maintenance system for our industrial equipment fleet. The system will leverage real-time sensor data (temperature, vibration, pressure) from thousands of machines across multiple sites to predict potential equipment failures before they occur. The project involves data preprocessing (handling missing values, outlier detection, feature engineering), model selection & training (using techniques like LSTM or Time Series Forecasting with TensorFlow/PyTorch), deployment of the model as a scalable API using cloud infrastructure (AWS or Azure), and creation of a user-friendly dashboard for visualizing predictions and maintenance schedules. A key focus will be on ensuring model accuracy, interpretability, and robustness to handle noisy data. Experience with implementing anomaly detection algorithms is highly desirable. The ultimate goal is to minimize downtime, reduce maintenance costs, and extend the lifespan of our equipment through proactive intervention. Strong understanding of statistical methods, time series analysis is a must. Deployment and monitoring using tools like Grafana or Prometheus is preferred. We need someone capable of building a production-ready, scalable solution that integrates seamlessly with our existing infrastructure. This role requires excellent communication and collaboration skills to work effectively with cross-functional teams including maintenance engineers, data scientists, and IT professionals. The final deliverable includes a fully functional API endpoint, documentation, training materials for the maintenance team and comprehensive testing results.