I’m building a production-ready web platform that blends a modern MERN stack with Python micro-services and AI modules. I already have the high-level architecture mapped out; what I need is a developer who can comfortably jump between JavaScript (React, Node, Express, MongoDB) and Python while keeping data engineering best practices front-and-center. The project flows like this: we pull data from several third-party APIs and internal sources, clean and transform it in real time, feed it to a set of AI services, then serve the insights back to users through an intuitive dashboard. Your day-to-day work will touch three key areas: • Data collection – build reliable connectors, handle auth flows, schedule recurring pulls, and maintain error logging. • Data processing – design ETL pipelines, implement transformation logic in Python (Pandas, PySpark or similar), and ensure everything is containerised for smooth deployment. • Data visualization – wire processed datasets into the React front-end, craft reusable chart components (D3, chart.js, or your preferred library), and optimise for performance. Acceptance criteria 1. End-to-end pipeline runs with a one-command deploy (Docker / docker-compose or comparable). 2. Clean, well-commented code repositories for both the MERN and Python services, backed by unit tests. 3. Dashboard renders live data with clear visualisations, passing UX handoff checks. 4. Concise setup documentation so another developer can reproduce the environment in under an hour. I value clear communication, frequent git commits, and pragmatic problem-solving. If you thrive in a stack-agnostic, data-heavy setting where AI features are integral rather than ornamental, let’s talk.