Machine Learning Engineer

Summary

Performed behavior analysis of animals based on a live video stream data from the user. Trained and compared various
models to detect various body parts and poses of the animals. Performed augmentations on the image dataset to
introduce random rotations and flips while training the model.
Optimized the models and achieved a best accuracy of 0.93 for animal pose classification and an IOU of 0.55 for
animal localization. Evaluated the models for sample videos and images on a Streamlit application.
Improved the accuracy from 0.93 to 0.95 of the model using Semi supervised Pseudo-labelling technique which
utilized 4k images as training dataset and 40k images as unlabelled data.

Expectations

Exciting problem statement to work in Machine learning/Data science domain. Able to contribute as well as improve my knowledge in predictive modelling and model deployment. Healthy working conditions for growth.

Employment Preferences

Expected Base Salary

**,000 USD

Academic Degree

Experience

Skills

• C++
• Python
• Machine Learning
• Deep Learning
• Data Analysis
• Data Modelling
• PyTorch
• PyTorch Lightning
• SQL
• Agile
• Git
• Git Actions
• CI
• CD Pipelines
• Coveralls