AI / ML Engineer - Industrial Analytics

Job description This role focuses on applying Artificial Intelligence and Machine Learning techniques to solve real-world industrial and operational challenges. The position involves working with large-scale sensor, telemetry, and time-series data to develop intelligent analytics solutions that improve system efficiency, reliability, and performance. Roles and Responsibilities Design, develop, and deploy machine learning models for industrial and operational analytics use cases, ensuring solutions are scalable, robust, and production-ready Develop predictive maintenance models to anticipate equipment failures and reduce unplanned downtime across industrial systems Build anomaly detection solutions to identify abnormal patterns in sensor and operational data, enabling early fault detection and proactive intervention Develop forecasting models for load, energy consumption, performance metrics, and operational trends using historical and real-time data Analyse large-scale sensor, telemetry, and time-series datasets , identifying patterns, correlations, and performance drivers Perform data pre-processing and feature engineering , including data cleaning, normalization, aggregation, and creation of domain-relevant features Design and maintain data pipelines for both real-time streaming data and batch data processing to support analytics and ML workflows Collaborate with cross-functional teams , including product, engineering, and domain experts, to translate business and operational problems into AI-driven solutions Optimize machine learning models for accuracy, computational efficiency, scalability, and deployment constraints Deploy models into production environments and integrate them with existing systems and applications Monitor model performance in production , track accuracy drift, data drift, and system behaviour, and implement continuous improvement strategies Document model logic, assumptions, workflows, and technical decisions to ensure maintainability and knowledge sharing Requirements: Strong foundation in Machine Learning and Statistics , including supervised and unsupervised learning techniques Proficiency in Python , with hands-on experience using libraries such as NumPy, Pandas, Scikit-learn, and PyTorch and/or TensorFlow Experience working with time-series data , including trend analysis, seasonality, and temporal modeling techniques Knowledge of predictive modeling, anomaly detection, and forecasting methods applicable to operational and industrial datasets Experience in data preprocessing and feature engineering , particularly for noisy and high-frequency data Familiarity with SQL and/or NoSQL databases for data extraction, storage, and analysis Understanding of data pipelines and ML workflows , including data ingestion, model training, validation, and deployment Good problem-solving, analytical, and debugging skills , with the ability to work on complex and ambiguous data challenges Good to have: Experience working with industrial, manufacturing, energy, or IoT datasets Exposure to real-time data processing frameworks and streaming architectures Familiarity with cloud-based ML platforms and scalable deployment practices Understanding of model monitoring, versioning, and MLOps concepts