Lead Machine Learning Engineer with experience in designing and deploying AI/ML systems for geospatial and sensor data. Skilled in Python, FastAPI, and cloud-based deployments, with a proven ability to integrate spatiotemporal AI/ML models and leverage geospatial data (LiDAR, satellite imagery) to enhance decision-making. Proficient in data exploration, analysis, and visualization using Python (Pandas, NumPy) and R, with expertise in developing and productionizing end-to-end ML/DL models including CNNs, RNNs, Unet, Unet++, DeepLabV3, Transformers, ViTs, and computer vision models such as YOLO with TensorFlow, PyTorch, and OpenCV. Strong background in geospatial analysis using Rasterio, GeoPandas, Shapely, GDAL, and GEE, along with time series forecasting, anomaly detection, and generative AI (Langchain, LangGraph, LlamaIndex). Experienced in integrating MLOps practices for automated deployment, monitoring, and retraining, leveraging AWS and Google Cloud for scalable processing and deployment. Skilled in vector databases, CI/CD pipelines, and geospatial visualization workflows.