We are seeking a senior SME – GIS & Remote Sensing with 8+ years of experience to lead the architecture and development of scalable geospatial models, automated pipelines, and spatial analytics. You will drive technical execution for large-scale water, agriculture, and climate security projects, mentoring junior engineers and interfacing with client leadership.

Key Responsibilities:

• Remote Sensing & Spatial Modeling: Design and deploy satellite-based models for crop classification, crop health, phenology tracking, and soil moisture estimation using Optical (Sentinel-2) and SAR (Sentinel-1) datasets.
• Hydrological & Climate Analytics: Perform surface water monitoring, conservation structure assessments, flood mapping, and watershed analytics.
• Pipeline Automation & Scalability: Build scalable, automated workflows and pipelines for large-scale satellite datasets using Python, Google Earth Engine (GEE), and PostGIS.
• Advanced Analytics & AI/ML: Apply machine learning, deep learning, time-series analytics, and rule-based modeling to geospatial data streams.
• GIS Engineering & Data Management: Oversee spatial database architecture, digitization, georeferencing, and multi-source tabular/spatial data integration.
• Validation & Quality Control: Validate predictive outputs against ground-truth field data to continuously refine model accuracy.
• Technical Leadership & Documentation: Author technical whitepapers, architecture designs, and methodologies; mentor junior analysts and present technical insights to stakeholders.

Preferred Skills :

• Work Experience: Minimum 8+ years of core experience in remote sensing, GIS analysis, and geospatial software development, with a proven track record of handling enterprise or government-scale projects.
• Education: M.Tech in Geoinformatics / M.Sc in Geospatial Engineering, Remote Sensing, Earth Observation, or Ph.D. in Agriculture, Geospatial Technology, Remote Sensing, or related fields.
• Satellite Data Expertise: Advanced expertise in processing Sentinel-1 (SAR) and Sentinel-2 (Optical) data, including multi-temporal analysis, crop phenology, and soil moisture estimation.
• Technical Stack & Tools: Proficiency in ArcMap, ERDAS Imagine, QGIS, Python, Google Earth Engine (GEE), and PostGIS.
• Development & Libraries: Deep experience with Python geospatial libraries including GDAL, Rasterio, GeoPandas, and xarray.
• Domain & AI Knowledge: Strong understanding of ML/DL algorithms, time-series analytics, spatial analytics, hydrology, and climate science.
• Leadership & Analytical Skills: Excellent problem-solving abilities, strong technical documentation skills, and experience mentoring team members and guiding external stakeholders.

Performance Indicators (KPIs):

• Model Accuracy & Validation: Achieve baseline accuracy, precision, and recall for satellite-based crop classification, phenology, and hydrological models validated against ground-truth data.
• Pipeline Automation & Efficiency: Reduce manual data handling by automating workflows using Python, GEE, and PostGIS, improving overall data processing
• Scalability & Data Infrastructure: Maintain stable, optimized spatial databases and pipeline execution handling multi-terabyte satellite datasets with zero critical failures during deployment.
• Hydrological & Agricultural Insights: Ensure timely and accurate delivery of actionable spatial outputs for flood mapping, surface water monitoring, soil moisture estimation, and crop health tracking.
• AI/ML Innovation & Implementation: Successfully deploy machine learning/deep learning and time-series analytics to refine complex spatial decision-support systems.
• Technical Documentation & Standard Compliance: Deliver of technical architecture specs, methodologies, and SOPs in alignment with project timelines and quality standards.
• Cross-Functional & Stakeholder Support: Provide technical guidance to internal teams, mentor junior analysts, and deliver actionable technical insights to clients and project stakeholders.