Supply Chain Technology Engineer

Company DescriptionDigital Mobile Africa (DMA Limited) is a B2B AgriFintech and Rural Commerce Aggregator focused on digitalizing Small to Medium Enterprises (SMEs) Agri-Traders, Agri-Processors, and Farmer Facing Organizations to efficiently connect with Rural Farmers. DMA empowers women and youth Agripreneurs to offer Climate-Smart Digital Extension Services, aggregated financial services, and access to payments, banking, insurance, and mobile money operator linkages. By partnering, DMA is building potentially the largest Rural Financial Services Distribution network, scalable across Africa. The company provides digital tools to Agri-Traders and Agripreneurs through its VIDA Soko Digital Marketplace, enabling smooth distribution of products and services within the Agribusiness Ecosystem.Role DescriptionThis is a full-time on-site role for a Supply Chain Technology Engineer, located in Dar es Salaam, Tanzania. The Supply Chain Technology Engineer will be responsible for analyzing, designing, and optimizing supply chain processes. Daily tasks include managing procurement activities, coordinating with various stakeholders, and implementing supply chain technology solutions. The role requires interfacing with suppliers, conducting market research, and ensuring the efficient movement of goods and services within the supply chain.QualificationsThe core technical expertise required centers on building and deploying scalable, reliable, and interpretable AI systems for a complex supply chain environment:Advanced Machine Learning (ML) & Deep Learning (DL):Predictive Analytics: Expertise in Time-Series Forecasting (for demand, price, logistics, weather, etc), Regression (for resource optimization), and Classification.Computer Vision (CV): Essential for analyzing supply chain structured and unstructured dataReinforcement Learning (RL): For dynamic supply chain optimization (e.g., re-routing logistics, real-time pricing models, inventory management).Data Engineering and Infrastructure:Big Data Architecture: Design and implementation of scalable, cloud-native data lakes and data pipelines to handle vast, diverse datasets (weather, satellite, transaction, IoT sensor data).IoT and Edge Computing: Expertise in integrating real-time data from sensors on vehicles, people, and deploying lightweight AI models at the "edge" (e.g., for instant hazard recognition on a mobile device).AI/ML Operations (MLOps):Implementing robust MLOps practices for continuous integration, continuous delivery (CI/CD), and monitoring of AI models to ensure performance, accuracy, and quick retraining in response to changing weather, market, or supply chain conditions.Blockchain Integration (for Supply Chain):Knowledge of how to design, build, and integrate the AI-OS with a distributed ledger (like blockchain) to provide transparent, immutable traceability of goods from source to market.