AI/ML Agricultural Engineer @ AgroNest

About the role

***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