Senior GeoAI Engineer
Organizational Context
The United Nations Economic and Social Commission for Western Asia (ESCWA) supports 22 Arab Member States in achieving inclusive and sustainable development through evidence-based policymaking. The Decision Support and Data Science Division (DSDS) designs and operates AI platforms and digital tools. This role will strengthen ESCWA's in-house geospatial analytics capacity by integrating GeoAI capabilities into DSDS platforms.
Job Purpose
This role is to design, build, and operationalize advanced geospatial AI pipelines, models, and services. The primary objective is to transform Earth observation and other geospatial data into actionable, policy-relevant indicators and decision-support tools for Arab Member States. The work will be integrated into existing DSDS platforms, enhancing the division's capacity for sophisticated geospatial analytics. This position is crucial for leveraging cutting-edge GeoAI technologies to address development challenges and inform policymaking within the region, contributing to ESCWA's mandate of promoting sustainable development.
Responsibilities
Design and implement a scalable, cloud-based geospatial data architecture for ingesting, processing, and serving raster and vector data using cloud-optimized formats and tools like Google Earth Engine or Microsoft Planetary Computer. Establish reproducible data pipelines and ensure interoperability with existing DSDS platforms via APIs and standard data services. Develop, fine-tune, and validate machine learning and deep learning models for priority use cases, adapting geospatial foundation models where beneficial. Document model performance, uncertainty, and limitations, validating against official statistics. Create interactive geospatial dashboards and map-based decision-support tools for policymakers, and produce subnational indicator datasets. Apply ESCWA's AI governance principles, ensuring data provenance, privacy, and bias assessment. Contribute technical content to practitioner manuals, design and deliver trainings, mentor staff, and provide technical input for project proposals.
Work Experience
Requires at least seven years of progressive experience in geospatial data science, remote sensing, or GIS engineering. This includes a minimum of four years developing and deploying machine learning/deep learning models on Earth observation data. Demonstrated experience building production-grade geospatial pipelines on cloud platforms is essential. Experience with geospatial foundation models, LLM-based geospatial workflows, or MLOps practices is desirable.
Skills
Proficiency in Python and the geospatial stack (GDAL, rasterio, xarray, GeoPandas, PostGIS). Expertise in deep learning frameworks (PyTorch or TensorFlow). Experience with segmentation, object detection, and time-series analysis of satellite imagery. Cloud platform geospatial pipeline development (Google Earth Engine, AWS, Azure, Planetary Computer). Geospatial foundation models and LLM-based geospatial workflows are desirable.