Key Responsibilities
Architect and Develop AI/ML Pipelines: Design and implement large-scale, production-grade AI/ML pipelines using appropriate tools and algorithms. Lead the full end-to-end ML lifecycle: model development, deployment, monitoring, and optimization for both on-prem and cloud environments.
Data Solution Design and Management: Define, develop, and sustain data solutions including storage, processing units, and user interfaces for industrial-grade applications. Build multi-tenant data collectors and storage systems. Implement streaming and batch data processing for near-real-time flows.
Data Modeling and Quality: Design scalable data models (SQL and NoSQL). Assess and improve data quality across incoming data flows. Apply data management and security best practices.
Analytics and Visualization: Develop customizable analytical dashboards for stakeholders. Ensure strong testing and quality assurance practices throughout development.
Innovation and Technology ...