Sr. Engineer
3 weeks ago
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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