• Data Engineering: Built robust data pipelines, Data lakes, and ETL processes to collect, preprocess, and transform data for analysis and modeling.• Performance Optimization: Optimized the performance and scalability of Al applications, ensuring real-time responsiveness.• Automation: Implemented automation solutions for repetitive tasks, optimizing efficiency and reducing manual workloads.• Recommendation Systems: Developed recommendation engines that personalize content, products, or services for users, optimizing engagement and satisfaction.• Al Integration: Collaborated with cross-functional teams to integrate Al and data science capabilities into existing systems and applications.• NLP and Chatbot Development: Lead the design, development, and maintenance of NLP-powered chatbots and virtual assistants to enhance user experiences and automate interactions.• Predictive Analytics: Designed and implemented predictive models to forecast trends, user behavior, and business outcomes, providing data-driven insights.

- Good Experience in communicating with users, other technical teams, and management to collect requirements, explain data infrastructure choices, and describe data engineering strategy-Prepared data pipelines in Azure Data factory for Benefit programs from different regions, demonstrating the workflow to the Business.-Hands-on experience in maintaining data Quality to analyze, profile, cleanse, & standardize the data, which helps maintain a 100% clean Database.-Working experience in designing and implementing complete end-to-end Hadoop infrastructure using MapReduce, Pig, Hive, Sqoop, Oozie, Flume, Spark, HBase, and Zookeeper.-Experienced in importing and exporting data using Sqoop from Relational Database system to HDFS and vice-versa.-Well Experienced in performing System Integrated Testing for high-level projects.-Good Experience in design, development, and implementation of highly scalable, high-volume software systems and components, source of truth systems for different business areas, developing and maintaining web services in an agile environment-- Built the infrastructure required for optimal extraction, transformation, and loading (ETL) of data from a wide variety of data sources-- Optimized data systems and built them from the ground up to support our data initiatives, increase operational efficiency, and improve collaboration and knowledge sharing.