Designed and built a scalable, high-performance, high- throughput valuation engine for valuation and
end of the day process for commodity trading (Oil, Gas and Power). Used in memory distributed
cache, Oracle, Core Java and Micro service architecture.

Built a tool for processing and classifying the customer feedback. Performed sentiment analysis,
name entity recognition and classification using NLP methods. Used Spacy, Stanford library and
Python.

Built and trained a convolutional neural network for end-to-end driving in a simulator, using
TensorFlow and Keras. Used optimization techniques such as regularization and dropout to
generalize the network for driving on multiple tracks. Many data augmentation techniques were
used.

Detected cars on a video stream. Used HOG as feature descriptor. Trained the network using Linear
SVM. Sliding window approach used for traversing image. Dataset provided by CrowdAI and Autti.