AI & Geospatial Systems Engineer
Building intelligent and decision support systems and solutions with Python, AI, and cutting-edge GIS technologies.
Geospatial intelligence, AI systems, and data platforms
Designed and deployed an internal knowledge assistant that answers complex questions from aviation regulations, SOPs, and PDFs with high accuracy. Implemented document auditing, structured chunking, vector search using ChromaDB, and controlled prompt strategies to prevent hallucinations. The system delivers grounded responses in 3–5 seconds, logs queries for evaluation, and exposes uncertainty instead of guessing, making it suitable for daily operational use.
Built a production-grade agricultural intelligence platform for crop health monitoring and decision support using Django, PostGIS, and Google Earth Engine. Implemented automated vegetation analytics (NDVI, EVI) combined with rule-based and ML-driven agronomic insights to support yield optimization, early stress detection, and intervention planning. Designed to operate at regional scale with near–real-time satellite ingestion and efficient spatial querying.
Developed a national-scale aviation infrastructure intelligence system using Django, PostGIS, and Leaflet. The platform supports spatial analysis of airports and airstrips for accessibility assessment, infrastructure planning, and airspace-related decision-making. Built with optimized spatial indexing and query pipelines to efficiently handle country-wide datasets and complex spatial queries.
Built a spatial analytics system to assess the distribution and accessibility of education infrastructure across Kenya using Flask, PostGIS, and Leaflet. The tool supports regional coverage analysis and planning insights, enabling stakeholders to identify spatial gaps and make evidence-based infrastructure decisions. Designed as a fast-deployable analytics solution for public-sector use cases.
Designing and delivering production-grade geospatial and AI systems across aviation, environmental, and public-sector domains
January 2025 – Present
March 2024 – December 2024
May 2023 – August 2023
Building production-grade geospatial, AI-driven, and data-intensive systems end-to-end
Spatial analysis, geoprocessing, and automation (ArcGIS, QGIS) · Large-scale satellite analytics & time-series processing (Google Earth Engine) · Image preprocessing and classification (ENVI, ERDAS Imagine)
Python backend systems (Django, Flask, FastAPI) · RESTful & async APIs for spatial and AI services · Spatial databases and query optimization (PostgreSQL/PostGIS)
Interactive WebGIS and dashboards (Leaflet, Mapbox, OpenLayers) · Modern frontend frameworks for data-heavy applications (Next.js, Vue.js)
Applied ML for spatial and environmental analysis (Scikit-learn, TensorFlow, PyTorch) · Retrieval-Augmented Generation (RAG), LLM orchestration, and hallucination control · Embeddings, vector search, and evaluation workflows
Spatial databases (PostGIS, SpatiaLite, Oracle Spatial) · Enterprise GIS data services (ArcSDE, GeoServer) · Vector databases for AI systems (ChromaDB)
Containerization and deployment (Docker) · Cloud platforms (AWS, GCP) · CI/CD pipelines, version control, and automation (Git, GitHub Actions)
Mobile field data collection, validation, and synchronization (ArcGIS Field Maps, Survey123, Kobo Collect, ODK)
Available for high-impact geospatial, AI, and decision intelligence projects