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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.

Manage recruitment, talent pipelines, onboarding, employee relations, HR compliance, HRMS operations, engagement, performance management, grievances, employer branding, and retention initiatives. Partner with hiring managers and business functions to improve employee experience, workplace culture, and people processes in a startup or high-growth environment.

Lead customer-facing AI and Industry 4.0 implementation projects for manufacturing plants. Responsibilities include change management, project planning, milestone tracking, AI-readiness assessments, stakeholder workshops, customer requirement gathering, vendor coordination, and delivery management. The role focuses on shopfloor digitalization, IIoT, automation, predictive maintenance, and predictive quality initiatives while building project processes and maintaining customer relationships.

Build and optimize scalable real-time and batch ETL/ELT pipelines for manufacturing analytics using Azure Databricks, PySpark, and distributed computing. Design cloud data lakes, lakehouses, and warehouses; process IoT, MES, SCADA, LIMS, and ERP data; implement CI/CD, DevOps, Docker, and Kubernetes practices; monitor pipeline reliability; and provide clean data for AI/ML models. The role also involves data preprocessing, cloud optimization, troubleshooting, and collaboration with automation, engineering, and data science teams.

Build and optimize scalable real-time and batch ETL/ELT pipelines using Azure Databricks, PySpark, and Apache Spark. Design cloud data lakes, lakehouses, and warehouses; process structured and unstructured industrial data; and apply data cleaning and transformation techniques. Deploy solutions across cloud platforms, automate workflows with DevOps and CI/CD, use Docker and Kubernetes, monitor pipeline reliability, and collaborate with data scientists and engineers supporting AI/ML models.