Sr. Engineer

7 days ago

Mumbai, India TIH | IIT Bombay Full time
Job Description:
Develop image-based classification models (CNNs, transfer learning, object detection, segmentation, vision).
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 with 3 - 5 years of experience.
OR
M.Tech with minimum 2 - 3 years of experience in Embedded system design.
Required Expertise:
Hands-on experience in Python.
ML/DL Frameworks - PyTorch, TensorFlow, Keras.
Proficiency in computer vision techniques – CNNs, object detection (YOLO/SSD), segmentation (U-Net/Mask RCNN), 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).
Preferred Skills:
Prior work in agriculture/agronomy-related AI projects.
Experience with cloud platforms (AWS/GCP/Azure).

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