About the program

This role focuses on developing advanced machine learning models tailored for agricultural applications such as crop yield prediction and plant disease detection. The work involves leveraging Pythonbased libraries, including Scikitlearn for classical ML algorithms, TensorFlow for deep learning architectures, and PyTorch for flexible model development. You will work with realworld agricultural datasets, ensuring proper preprocessing, feature engineering, and data augmentation to enhance model accuracy. Model training, evaluation, and performance optimization will be key responsibilities, using metrics relevant to agricultural outcomes. Additionally, the role requires assisting in the deployment of trained models as APIs, enabling seamless integration into agricultural platforms or applications. Postdeployment, you will help monitor model performance, identify potential drift, and implement necessary updates. This work directly contributes to improving farming decisions, increasing productivity, and supporting sustainable agriculture through technologydriven solutions. Collaboration with domain experts will ensure models are both technically sound and practically useful.

Perks

nil

Who can apply?

Only those candidates can apply who:

1. are from B.Tech. / B.E./ Equivalent
2. and specialisation from Artifical Intelligence and Machine Learning
3. are available for duration of 6 Months
4. have relevant skills and interests

Terms of Engagement

nil

Number of openings

1