Overview

About AgroNest Ventures:

Responsibilities

• Model Architecture & Training: Build, train, and optimize state-of-the-art AI/ML models for crop yield prediction, early-stage pest/disease detection, soil health telemetry, and autonomous irrigation.
• Multimodal Data Fusion: Ingest, preprocess, and align large-scale, heterogeneous datasets including multispectral satellite data (Sentinel/Landsat), drone imagery, weather feeds, and ground IoT sensor streams.
• Edge AI Deployment: Optimize deep learning architectures (e.g., CNNs, Vision Transformers) to run efficiently on resource-constrained Edge IoT devices, drones, and smartphone applications for low-connectivity environments.
• Cross-Disciplinary R&D: Collaborate deeply with agronomists, IoT hardware engineers, and product managers to map real-world agronomic challenges into robust mathematical and machine learning frameworks.
• Pipeline Scalability: Architect, monitor, and scale secure data pipelines and MLOps workflows to support continuous integration and real-time inference at millions of data points.

Qualifications

• Education: Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Science, Agricultural Engineering, Remote Sensing, or a highly quantitative STEM field.
• Core Tech Stack: Advanced proficiency in Python and deep learning frameworks (PyTorch, TensorFlow) along with standard ML libraries (Scikit-Learn, NumPy, Pandas).
• Domain Expertise: 2+ years of direct experience working with geospatial data processing tools (GDAL, Rasterio, Shapely, QGIS) and handling spatial imagery datasets.
• Edge & Cloud Deployment: Proven hands-on experience deploying models to production via AWS/GCP, Docker, and optimization tools like TensorRT or ONNX for edge deployment.
• Problem-Solving Mindset: Strong foundation in statistics, probability, and linear algebra, combined with a fierce curiosity for biological systems and agronomic sciences.

Benefits

• Highly competitive market salary plus valuable, early-stage equity options (ESOPs).
• The opportunity to work with real, massive-scale field data from over 300,000+ farmers instead of clean, synthetic datasets.
• Collaborative culture working directly alongside visionaries in AI, IoT, and global XaaS ecosystems.
• Medical benefits, flexible hybrid working model, and rapid-track career progression to tech leadership.
• Cell phone reimbursement
• Commuter assistance
• Flexible schedule
• Food provided
• Health insurance
• Internet reimbursement
• Leave encashment
• Life insurance
• Paid sick time
• Paid time off
• Provident Fund
• Work from home