We are seeking a highly qualified Senior Agricultural Engineer with a strong academic background in agronomy, crop science, irrigation engineering, or related agricultural sciences, combined with extensive hands-on experience in precision agriculture and smart farming technologies.
The ideal candidate will possess deep scientific knowledge of crop production systems, soil–water–plant relationships, and modern agricultural management practices, along with demonstrated experience in applying satellite imagery, remote sensing, geospatial analytics, and data-driven technologies to real-world agricultural operations.
This position requires an individual capable of bridging advanced agricultural science with emerging digital technologies, contributing to the design, implementation, and optimization of intelligent farming and irrigation systems.
Key Responsibilities

• Agronomy & Crop Management: Optimize crop production, soil and water management, crop health, and resource efficiency.
• Precision Agriculture & Smart Farming: Design and implement smart farming solutions using IoT sensors, automated irrigation systems, and digital farm management platforms.
• Satellite Imagery & Remote Sensing: Analyze Sentinel and Landsat imagery, vegetation indices (NDVI, NDRE, EVI), and geospatial data to monitor crop health, water stress, and field variability.
• Smart Irrigation & Water Management: Optimize irrigation scheduling using crop water requirements, soil moisture, weather data, and evapotranspiration models, preferably for center-pivot irrigation systems.
• Research & Data Analysis: Design field experiments, conduct statistical analysis, validate agricultural technologies, and collaborate with software, data science, and IoT teams.

Required Qualifications

• Master's degree or higher in Agronomy, Crop Science, Agricultural Engineering, Irrigation Engineering, or a related field; Ph.D. is strongly preferred.
• 5 years of relevant professional experience, including 3–5 years in precision agriculture, smart farming, or data-driven crop management.
• Proven hands-on experience with satellite imagery, remote sensing, and geospatial analysis in agricultural applications.
• Strong knowledge of agronomy, crop physiology, irrigation management, GIS, and scientific data analysis.
• Python or R proficiency is an advantage.

Key Competencies

• Scientific and analytical thinking
• Practical application of agricultural research
• Technical judgment and problem-solving
• Experimental design and field validation
• Cross-functional collaboration
• Technical documentation and project management

Key Screening Criteria

• Candidates must demonstrate practical experience in real-world smart agriculture projects, particularly using satellite imagery and geospatial data for agricultural monitoring, analysis, and decision-making. Academic qualifications alone are insufficient.

Preferred Experience: Center-pivot irrigation systems, integration of satellite data with field sensors and weather data, crop simulation models (AquaCrop, DSSAT), and AI-assisted agricultural analytics.