Teams at TVARIT

Build and optimize scalable real-time and batch ETL/ELT pipelines for industrial manufacturing data. Design cloud-based data lakes, lakehouses, and warehouses using Azure Databricks, PySpark, and Apache Spark. Process data from IoT, MES, SCADA, LIMS, and ERP systems; ensure quality, security, reliability, and performance. Automate workflows with CI/CD and DevOps practices, use Docker and Kubernetes, and collaborate with automation teams, data scientists, and engineers to support AI/ML models.

Develop and deploy machine learning and statistical models for predictive quality, maintenance, and energy optimization in metal manufacturing. Analyze industrial time-series data, engineer features, validate models, and collaborate with plant engineers and customers. Contribute to data onboarding, deployment, monitoring, reusable Python libraries, and MLOps practices. Mentor junior data scientists and communicate technical findings to technical and non-technical stakeholders.

Develop and deploy scalable machine learning solutions for industrial customers, from requirements gathering through production monitoring. Responsibilities include exploratory data analysis, feature engineering, Python development, model building, cloud deployment, MLOps, client presentations, technical collaboration, and mentoring junior data scientists. The role focuses on predictive quality, maintenance, energy reduction, and other AI applications in manufacturing and metal industries.