1. Design, develop, test, deploy, maintain & document innovative solutions for challenging problems with robust, scalable, reusable, efficient, production-quality software
2. To identify, propose and build infrastructure, large-scale data pipelines, data storage strategy, common libraries and useful tools needed to manipulate data so as to create inputs for deep learning algorithms
3.Perform statistical analysis and fine-tuning using test results
4.Train and retrain systems when necessary
5.Extend existing ML/DL libraries and frameworks
6.Understanding of data structures, data modeling, and software architecture
7.Deep knowledge of maths, probability, statistics, and algorithms
8.Ability to write robust code in Python, Java, and R
9.Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)
10. Keep abreast of developments in the field
11.An intense sense of ownership, initiative-taking, and a can-do attitude
12.Great attention to detail and a data-driven approach to problem-solving
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