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.
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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Sr Engineer • Mumbai, India
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