About AgroNest Ventures:
AgroNest Ventures Private Limited is a deep-tech powerhouse pioneering scalable, technology-driven solutions for real-world resilience. By fusing cutting-edge Artificial Intelligence, proprietary IoT architectures, and XaaS (Everything-as-a-Service) business models, we build tech ecosystems that empower millions of farmers and agribusinesses globally. Having already scaled to impact over 300,000+ farmers, we are expanding our core engineering division to build next-generation predictive and autonomous agricultural models.
Role Overview:
As an AI/ML Agricultural Engineer, you will sit at the intersection of advanced computer science and agronomy. You will design, build, and deploy production-grade computer vision, deep learning, and predictive models that translate raw agricultural data, including satellite imagery, drone scans, real-time IoT telemetry, and weather data into highly accurate, actionable intelligence for field-level execution. This is a high-ownership role where your code directly affects global food security, climate resilience, and sustainable farm yields.
Key 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 & Engineering Depth:
- 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.
What We Offer:
- 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.
Pay: ₹409,769.03 - ₹1,699,669.63 per year
Benefits:
- 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
Work Location: In person
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