Over 3+ years of comprehensive experience as a Machine Learning Engineer building and deploying machine learning models in production. Knowledge in Application Modernisation & Serverless Cloud services such as Amazon Web Services & Google Cloud Platform and Microsoft Azure AIML stack. Domain Understanding - Manufacturing, Insurance, HealthCare, Government Bodies, Financial Services & Banking, Retail, Automotives, Power & Energy, Telecommunication, IoT & Fintech. Proficient in applying Statistical Modelling and Machine Learning techniques (Linear Regression, Logistic Regression, Decision Trees, Random Forest, SVM, K-Nearest Neighbors, Bayesian, XG Boost) in Forecasting/ Predictive Analytics, Segmentation methodologies, Regression based models, Factor analysis, PCA, Ensembles and good knowledge on Recommendation Systems. Using Agile methodology to develop a project when working on a team. Expert in Python libraries such as NumPy, Scipy for mathematical calculations, Pandas for data preprocessing/wrangling, MatPlotLib, Seaborn for data visualization, Sklearn for machine learning, TensorFlow, Keras for Deep leaning and NLTK for NLP. Expert in using Model Pipelines to automate the tasks and put models into production quickly. Expertise in Dimensionality Reduction techniques like PCA, LDA, Singular Value Decomposition technique. Expertise in k-Fold Cross Validation and Grid Search for Model Selection. Practically engaged in Evaluating Models performance using A/B Testing, K-fold cross validation, R-Square, CAP Curve, Confusion Matrix, ROC plot, Gini Coefficient and Grid Search. Good Knowledge on Version control systems such as Git, SVN, Github, bitbucket. Data Lake Modernization & Data Warehousing using cloud services. API handling - REST, HTTP, GET, POST, PostMan.