Building deep learning models as specified using TensorFlow/PyTorch
Collaborating with the team to ensure that models are properly tested and deployed
Good knowledge/understanding of Deep Learning Algorithms in both NLP (LSTM’s, RNN’s etc.) and Computer Vision (CNN’s etc.) domain and TensorFlow and deep learning frameworks like Keras, PyTorch - to solve real-life problems
Creating and updating documentation for the work done.
Understanding of deep learning algorithms implemenation from scratch (LSTM,RNNS,DNNS,BI-LSTMS etc)
Computer Vision and Image processing.
TRANSFER LEARNING - GAN’s
Master or PhD degree in Statistics, Applied Mathematics, or similar quantitative discipline, or equivalent practical experience.
Relevant internship or work experience with data analysis.
Experience with statistical software (R, Python, S-Plus, SAS, or similar) and experience with database languages (e.g. SQL).
Master Or B.tech other relavants
Role and Responsibilties
Research on given machine learning/data science topics for writing articles for our company websites
Assist with hands-on coding on Jupyter Notebook for providing code snippets in the articles
Work on short projects & assignments
Work on deployment techniques
Design and implement machine learning, information extraction, probabilistic matching algorithms, and models
Use your passion for programming and deep learning/AI skills using Python, TensorFlow, and deep learning frameworks like Keras, PyTorch to solve real-life problems
Develop AI solutions in the computer vision domain using convolutional neural networks (CNN) and a combination of deep neural networks such as CNN & RNN
Solve object detection, recognition, image classification problems using machine learning and deep learning algorithms & transfer learning methods and techniques
Use innovative & automated approaches for data annotation, labeling, and data augmentation & use active learning
Handle online code assistance, code mentoring, and education-based solutions
Certificate, Letter of recommendation, Flexible work hours, Informal dress code, 5 days a week, Free snacks & beverages.