Full Stack Developer

Job Description for Full Stack Developer We are seeking a highly skilled Full Stack Developer to design, build, and maintain interactive DSS, platforms and dashboards that integrates remote sensing data, AI-driven analytics, and real-time environmental monitoring. Key Responsibilities: - Develop and maintain scalable web-based applications and dashboards to visualize urban heating patterns, aquaculture mapping and real-time analytics. - Design robust back-end systems to manage large geospatial datasets, real-time sensor feeds, and AI model outputs. - Work with data scientists and image analysts to integrate remote sensing products, AI results, and spatial layers into the application. - Implement APIs for data ingestion, processing, and delivery from various sources (e.g., satellites, IoT sensors, weather systems). - Ensure responsive front-end design with intuitive visualizations of heat maps, time-series graphs, and spatial analytics. - Optimize application performance, security, and scalability in cloud or hybrid environments. - Collaborate with GIS team and contribute to technical documentation, version control, and continuous integration pipelines. Requirements - Master’s degree in Computer Science, Software Engineering, Geo informatics, Geospatial Data Science or related field. - 3-5 years of experience in full-stack web development with proven experience in data-heavy applications. - Strong proficiency in Python for back-end development and data processing (Flask, Django, or FastAPI); experience building services that integrate AI/ML outputs. - Proven experience with Node.js, Express.js and RESTful API development for building backend services. - Hands-on experience with front-end technologies (React.js) data visualization libraries and mapping frameworks such as D3.js, Leaflet, Mapbox GL, or Deck.gl. - Experience working with spatial databases (PostGIS, MongoDB, etc.) and cloud services (AWS, Azure, GCP). - Understanding of remote sensing data structures, raster/ vector handling, and integration of geospatial analytics. - Familiarity with AI/ML model deployment and real-time data integration (e.g., MQTT, WebSockets). - Familiarity with ETL based Workflows (Preferable)