Machine Learning Engineer
Current• Utilized collaborative filtering, content-based filtering, and matrix factorization techniques to create a hybrid personalized recommendation system. • Used K-fold cross Validation technique to improve model performance and to test the model on the sample data before finalizing the model.• Used NLTK and Text blob libraries with python to perform sentiment analysis for customer data and Gen AI algorithms are employed to analyze the integrated data and generate actionable insights.• Worked with public/private Cloud Computing technologies (IaaS, PaaS &SaaS) and Microsoft Azure and worked for customer analytics and predictions.• Used Power BI and Tableau for Business Intelligence tool for visually analyzing the data and to shows the trends, variations, and density of the data in form of graphs and charts.• Built and maintained SQL scripts, indexes, and complex queries for data analysis and extraction.• Created and executed complex SQL statements in both SQL production and development environments.• Used scikit-learn, Pandas, and the stats models Python libraries to build predictive forecasting for time series analysis.•Developed a chatbot powered by Open AI’s GPT-3.5 (LLM type) to handle customer inquiries in online retail setting and created a generative AI model that generates compelling product descriptions based on existing product details and attributes. Extended the chatbot capabilities to provide personalized product recommendations.• Formulated procedures for integration of python, R programming with data sources and delivery systems.• Used query languages such as SQL and experience with NoSQL databases, such as MongoDB, GrapghDB.• Worked with both unstructured/structured data Machine Learning Algorithms such as Linear, Logistic, Decision Tress, Random Forests, Support Vector Machines, Neural Networks, KNN, and Time series analysis.• Automated model selection and hyper parameter tuning using Azure autoML