Geospatial Engineer

Job Description

We are looking for Geospatial AI Engineer (Satellite, Drone & LiDAR Data Processing) Location - Mumbai. (WFO). Role Overview: We are looking for a Geospatial AI Engineer with hands-on experience in processing satellite, drone, and LiDAR data and deploying AI models for geospatial applications. The ideal candidate should have expertise in remote sensing, deep learning, and scalable AI deployment for real-world geospatial use cases. Key Responsibilities: - Geospatial Data Processing: Preprocess and analyze satellite, drone, and LiDAR datasets using GIS and remote sensing tools. - AI Model Development: Build, train, and fine-tune AI models for object detection, segmentation, land use classification, and anomaly detection in geospatial data. - Model Deployment & Optimization: Deploy geospatial AI models on cloud (AWS/GCP) and edge devices (Jetson, embedded systems), ensuring scalability and efficiency. - Geospatial Visualization: Utilize GIS tools (ArcGIS, QGIS, Google Earth Engine) to visualize and interpret model outputs. - Data Pipeline Automation: Develop end-to-end pipelines for geospatial AI workflows, integrating cloud storage, inference, and post-processing. - Collaboration: Work with GIS specialists, remote sensing experts, and software engineers to develop production-ready AI solutions. Required Skills: - Remote Sensing & GIS: Hands-on experience in processing and analyzing satellite, drone, and LiDAR data. - AI/ML for Geospatial Data: Proven experience in deep learning frameworks (TensorFlow, PyTorch) for geospatial applications. - Programming: Strong Python skills (Geopandas, Rasterio, OpenCV, PyTorch/TensorFlow). - Geospatial Data Processing: Experience with GDAL, PDAL, Google Earth Engine, and spatial databases (PostGIS). - Model Deployment: Hands-on experience deploying AI models on AWS/GCP/Azure and edge devices like NVIDIA Jetson. - End-to-End AI Solutions: Prior experience in deploying and maintaining geospatial AI models in real-world applications. Preferred Experience: - Proven track record of deployed models for land use classification, vegetation classification, deforestation monitoring, infrastructure mapping, flood prediction, etc. - Experience in optimizing AI models for real-time inference on cloud and edge computing platforms. - Exposure to hyperspectral/multispectral imagery and SAR data processing. - Knowledge of MLOps for managing geospatial AI pipelines. Education & Qualifications: Bachelor's/Masters degree in Computer Science, GIS, Remote Sensing, AI/ML, or a related field. 3+ years of industry experience with hands-on model deployment.