About

Laxman Singh Tomar is a Senior NLP Engineer at Emplay Inc. with a passion for democratizing Artificial Intelligence and shaping the next generation of products in industry verticals such as healthcare, finance, social media, and law. With 3.61 years of relevant experience, he has developed expertise in several technologies and tools such as NLP, ML, XGBoost, Keras, Sagemaker, AWS, Seaborn, Deep Learning, Scikit, AI, Tensorflow, Pandas, Pytorch, Docker, Research Scientist, Word2Vec, Snowflake, Frontend, QA, Python, Kubernetes, SQL, Microservices, Text Classification, Random Forest, ECS, Hyperparameter, Bag of Words, OpenCV, Apache Airflow, RabbitMQ, Neural Networks, Elastic Search, Mobile, Backend, and Web. Laxman is a skilled Machine Learning Engineer who is proficient in data acquisition using public datasets, scraping, product intervention, and data augmentation. He is also experienced in data analysis using Pandas and Dask, extensive feature engineering, training and testing machine learning & deep learning models using Scikit-Learn, XGBoost, Keras, Tensorflow, and Pytorch, and expressive and insights-driven data visualizations using Matplotlib, Seaborn, and Dexplot. He is adept at rapid & scalable development using AWS Sagemaker. Currently, Laxman is co-creating the Voicenet, an Open Source library that makes it easy to develop, manage, and handle voice-based tasks such as Speech to Text, Gender, Age, and Emotion Detection, etc. using speech samples. He has also worked as an NLP Engineer at Emplay and as a Machine Learning Engineer at Albot Technologies, where he led the build of SafeNet, an AI-powered extension meant to detect & handle visual, toxic content prevalent over the web in order to safeguard the internet browsing experience of children. He has also worked on building Cyber Anomaly Detection System, an ML-based tool to automatically detect network anomalies and 14 different types of attacks such as DoS, DDoS, Heartbleed, and others in real-time, Homoglyphs Phishing Detection System, an ML-based tool to automatically detect visually similar phishing domains via CNNs and to safeguard user information, and Email Phishing Detection, an ML-based tool to analyze various properties of raw email files and predict whether incoming mails are phishing emails or not in real