• Assist in leveraging and fine-tuning LLM pretrained models with historical data for text classification and other AI/ML tasks.• Support the implementation of AI/ML systems on AWS using services like Amazon S3, EC2, Lambda, and SageMaker, and help manage MLOps workflows with MLFlow.• Extract, transform, and load data from various sources into Azure Data Storage services using Azure Data Factory, T-SQL, and U-SQL.• Implement and manage batch jobs using SQL Server Integration Services (SSIS) for processing data from multiple external sources.• Profile data to identify quality issues and collaborate with teams to address these issues, using tools like Python and Pandas in Jupyter notebooks.• Create unit test cases, prepare test plans, and coordinate with UAT teams to ensure thorough testing and documentation of changes.• Develop and automate complex reports and dashboards to meet business requirements, using tools like SSAS and SQL.

• Designed and implemented data pipelines using AWS Glue to extract, transform, and load customer data from various sources, including online banking systems and credit card transactions.• Built and maintained a data warehouse using Amazon Redshift to support business intelligence and analytics. reporting retail banking division.• Collaborated with data scientists to develop and deploy machine learning models using AWS SageMaker to improve credit risk prediction accuracy and automate customer segmentation.• Implemented data quality controls to ensure data integrity and consistency across multiple systems.• Worked with cross-functional teams to design and implement data governance policies and procedures to ensure compliance with regulatory requirements.• Developed and maintained ETL workflows using Apache NiFi to automate the extraction of data from various sources.• Improved data freshness by 30% through the implementation of incremental data loading and scheduling using AWS Glue.• Participated in the migration data infrastructure to the cloud, including the design and implementation of cloud-based data lakes and warehouses using AWS S3 and Amazon Redshift

• Conducted data analysis on cancer datasets to identify patterns and develop predictive models.• Utilized machine learning algorithms to classify cancer as benign or malignant, achieving an accuracy of 95%.• Developed visualizations and reports to communicate findings to stakeholders, facilitating data-driven decisions.

• Analyzed sales and marketing data to identify trends and patterns, providing actionable insights that increased revenue by 15%.• Conducted A/B testing on website features. leading toa 10% improvement in user engagement.• Developed interactive dashboards and reports in Power Bl, translating complex data into clear and actionable insights for stakeholders leading to a 20% increase in data-driven decision-making across the organization.• Developed end-to-end ETL (Extract, Transform, Load) pipelines using Azur Data Factory and Azure Databricks. ensuring• seamless data ingestion, transformation, and loading from various sources into Azure SQL Data Warehouse• Engineered data pipelines using Apache Kafka and Apache Flink enabling real-time data processing with99.9% uptime,• Developed and deployed machine learning models for customer segmentation using tools like scikit-learn or Keras, achieving a 30% increase in campaign conversion rates.• Participated in the development of a big data solution using Hadoop and Hive, enabling large-scale data analysis and reducing processing time by 20%.