Applied ML - Engineer

• 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 400076.