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Job Description: Development, adaptation, and implementation of AI/ML algorithms and frameworks, Prediction algorithms Developing deep learning and machine learning algorithms (CNN, object detection, segmentation, SVM, AE) Time series forecasting: AR, ARIMA, SARIMA, ES, Prophet, LSTM Conduct data preprocessing, augmentation, and annotation workflows for image datasets. Design, train, and validate deep learning architectures for feature identification using CNN, ResNet, EfficientNet, YOLO, U-Net, Mask R-CNN, ViT/Swin Transformer. Develop clean, modular, and production-ready code for model training, inference, and deployment. Collaborate with domain experts to translate agricultural knowledge into AI models. Support integration of models with mobile application (through APIs and deployment-ready formats like TensorFlow Lite / ONNX). Write unit tests, integration tests, and documentation to support long-term use of the framework. Document methodologies, benchmarking reports, and prepare technical handover materials. Minimum Qualifications and Experience: B.Tech in Computer Science, Electronics and Communications or any related field with 3-5 years of relevant experience OR M.Tech in Computer Science, Electronics and Communications or any related field with 2-3 years of relevant experience Required Expertise: Strong hands-on experience with Python and ML/DL frameworks (PyTorch, TensorFlow, Keras). Proficiency in computer vision techniques – CNNs, object detection (YOLO/SSD), segmentation (U-Net/Mask R-CNN), Vision Transformers (ViT, Swin Transformer, DeiT). Libraries: NumPy, Pandas, OpenCV, Scikit-learn, Matplotlib/Seaborn. Knowledge of model optimization for deployment (quantization, pruning, TensorFlow Lite, ONNX). Experience in developing APIs (Flask/FastAPI) for model serving. Familiarity with ETL processes, data pipelines, and statistical validation methods. Basic understanding of Docker and version control (Git) and experience with MLOps tools Ability to write production-grade Python code following best practices (modular design, logging, testing, error handling) Understanding of statistical analysis such as normality test, dicky fuller test etc Location of work: TIH-IoT, IIT Bombay Campus, Powai, Mumbai .