Sravani Rao

• Developed data lakehouse architecture that allowed for unified data access, combining the strengths of data lakes and data warehouses for enhanced data storage efficiency. • Implemented advanced data cleaning frameworks that significantly improved the quality of datasets used for LLM training, ensuring high relevance and accuracy. • Conducted extensive analysis of embedding techniques, improving data representation and relevance in search queries for generative AI systems. • Developed innovative data processing workflows that integrated various data sources, ensuring comprehensive coverage for LLM training. • Established a protocol for data quality assessment, ensuring all datasets met high standards before integration into machine learning models.

• Developed data lakehouse architecture that allowed for unified data access, combining the strengths of data lakes and data warehouses for enhanced data storage efficiency. • Implemented advanced data cleaning frameworks that significantly improved the quality of datasets used for LLM training, ensuring high relevance and accuracy. • Conducted extensive analysis of embedding techniques, improving data representation and relevance in search queries for generative AI systems. • Developed innovative data processing workflows that integrated various data sources, ensuring comprehensive coverage for LLM training. • Established a protocol for data quality assessment, ensuring all datasets met high standards before integration into machine learning models.

• Worked on image classification using CNN and computer vision, Implemented Hyper parameter tuning for scaling performance and achieved over 87% accuracy Image identifying. • Applied various machine learning algorithms and statistical modeling techniques like decision trees, Naïve Bayes, Principal Component Analysis, regression models, Artificial Neural Network, clustering, SVM to identify Volume using scikit-learn packages in Python, Pyspark. • Developed keyword extraction models using range of tools including TF-IDF, word2vec, NLTK and other NLP packages. • Implemented parallelized data processing operations using DASK framework to clean and filter text data using python and/or Pyspark. • Responsible for SQL Server Reporting Services Planning, Architecture, Training, Support, and Administration in Development, Test and Production Environments

• Experience in fact dimensional modeling (Star schema, Snowflake schema), transactional modeling and SCD (Slowly changing dimension). Process and load bound and unbound Data from Google pub/subtopic to Big Query using cloud Dataflow with Python. • Extensive experience in Text Analytics, developing different Statistical Machine Learning, Data mining solutions to various business problems and generating data visualizations using R, Python, and Tableau. Used with other packages such as Beautiful Soup for data parsing. • Involved in the development of Web Services using SOAP for sending and getting data from the external interface in the XML format. Used with other packages such as Beautiful Soup for data parsing. • Worked on the development of SQL and stored procedures on MYSQL. Analyzed the code completely and reduced the code redundancy to the optimal level. Design and build a text classification application using different text classification models.

• Design, build and validated Crude oil production prediction ML models and optimized the hyperparameters for best model. • Developed data visualization using Cross tabs, Histogram, Heat maps, Box and Whisker charts, Scatter Plots, Geographic Map, Pie Charts and Bar Charts and Density Chart in Tableau. • Worked extensively with Advance analysis Actions, Calculations, Parameters, Background images, Maps, Trend Lines, Statistics, and table calculations. • Identify & clean the noise from the structured and unstructured data collected from various sources.