An enthusiastic and passionate machine learning engineer who finds hidden information and patterns in data and solves different problems with domain knowledge. More than 3 and half years of industry experience. Strong programming background in Python and domain knowledge of HVAC systems. Handles all machine learning operations(Mlops with architecture and implementation from scratch) including Cuda configurations for deep learning, and building web services in Python for model retraining and monitoring. Dockerize those web services and deploy them on cloud and edge devices. Automate stuff with Python to save time.Use different cloud services daily (aws ec2 Linux GPU instance, lambda, s3, sam, code-commit(git) and cloud watch/event bridges etc) for data fetching, preprocessing, wrangling, storing, model building and deployment.Research interest led me to be Co-author of the Research paper "Deep Learning-based Predictive Modeling of Building Energy Usage" in the 2023 6th International Conference on Energy Conservation and Efficiency (ICECE) on IEEE Xplore. Paper Link -> https://ieeexplore.ieee.org/abstract/document/10092502Love to read books, learn new technologies, solve complex problems, help others and travel.Reach out to me if you are stuck and need help.Looking forward to working on different use cases and domains in the world of AI/Ml/Data Science.Skills: machine learning, deep learning, regression, classification, clustering, time series forecasting, Python, data analysis, data cleaning, data preprocessing, data wrangling/feature engineering, data visualizations, building predictive models, deploying models, preparing ml environments, neural networks (ANN, CNN, RNN, LSTM), Mlops, programming, problem-solving, fast API, AWS, GCP, docker, git and Linux.