AI / ML Engineer - Industrial Analytics

Job highlights

• 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 .
• Experience working with industrial,manufacturing,energy,or IoT datasets . .

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