Summary
✨ AI‑Generated
An experienced backend-focused engineer is sought to help build a data-driven digital marketplace addressing inefficiencies in the food supply chain. The role offers the chance to work on technology that connects producers and buyers in real time, using operational data to improve transactions, reduce waste, and support more sustainable commerce.
Highlights
Opportunity to work on mission-driven technology addressing food supply-chain inefficiencies, with a focus on data-driven marketplace systems and technology designed to improve access, efficiency, and sustainability.
Description
Company Description
R-ainbow Marketplace is a mission-driven digital platform focused on empowering the 525 million smallholder farmers who produce most of the world’s food yet often sell at a loss.
The company is transforming the food supply chain and food tech industry by reducing waste caused by outdated distribution systems and market inefficiencies, which can result in 30–40% of harvests being lost.
R-ainbow operates as an online marketplace that connects small producers with buyers in real time, using data on product availability, location, and stock levels to enable efficient, cost-effective transactions.
The app makes buying and selling produce more accessible, convenient, and sustainable, and can be used anywhere.
R-ainbow has been recognized as a 2023 EU Tech Chamber SDG Award finalist for “Zero Hunger” and was honored in 2024 at Westminster Abbey in the historical book “The Commonwealth at 75.”
Role Description
We are looking for an experienced backend developer who can build scalable backend systems and work closely with AI models.
The role requires someone who understands both traditional backend development and applied AI, including using company data to train, fine-tune, evaluate, and improve models.
Key responsibilities
Design and build secure, scalable backend services and APIsImprove API speed, reliability, database performance, and resource usageWork with third-party, self-hosted, and in-house AI modelsPrepare, structure, and manage internal data for AI trainingBuild model training, fine-tuning, evaluation, and inference pipelinesUse techniques such as RAG, embeddings, semantic search, and vector databasesConnect AI models with live business data and backend servicesBuild background jobs, queues, caching, event tracking, and monitoringMeasure model accuracy, response time, cost, and output qualityPrevent AI models from returning incorrect or unverified business dataHelp decide whether to use external models, open-source models, or internally trained modelsCreate a backend foundation that can support growing data, traffic, and AI use cases
