AI/ML Agricultural Engineer @ AgroNest (Mumbai)

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: Bachelors, Masters, 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. Compensation: 409,769.03 - 1,699,669.63 per year Benefits: - Cell phone reimbursement - Commuter assistance - Adaptable 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 .