• Lead the effort to create an Enterprise Data Lake on Azure Cloud. Consolidation of various data sources from GCP and AWS to establish ‘single version of truth’ and enable efficient analytics capabilities.• Developed and operationalized Retrieval Augmented Generation (RAG) frameworks to enhance natural language processing capabilities.• Integrated AI/ML cloud services like SageMaker or Azure ML into existing workflows, improving scalability and performance.• Developed applications for banking systems, focusing on automation software and model fine-tuning to enhance operational efficiency.• Hands on AI/ML frameworks such as TensorFlow and PyTorch to design and implement robust models that address specific banking needs.• Working on prompt techniques with different scenarios to train Chat Bot with accurate answers and how to give genuine prompt without errors.

· Part of a dynamic and diverse team comprised of data scientists, engineers, developers, and product managers, working strategically to harness the wealth of data available in our ecosystem, aiming to leverage ML and AI technologies to enhance customer satisfaction.· Leading multiple AI initiatives across our product suite, including Predictive Analytics services for baselining and forecasting performance and security KPIs, Security Analytics with anomaly detection capabilities, utilizing technologies such as NLP, LSTM, KubeFlow, Docker, AWS Sagemaker, and AWS Greengrass.· Overseeing a comprehensive range of project management tasks, including approving project plans, prioritizing activities, managing engagements, allocating resources, planning budgets, aligning stakeholder expectations, and ensuring successful project outcomes.· Optimized LLM performance through techniques such as model compression, parallel processing, and algorithmic optimizations.· Integrated NLP models into production systems and applications, deploying them in scalable and reliable environments, and optimizing for performance and resource efficiency.· Managing data storage and processing pipelines in GCP to support AI and ML services across production, development, and testing phases, employing SQL, Spark, Python, and AI VM technologies.

· Responsible for building Data Models and preparing refined data for Business users through the ETL process.· Installed and Administered Oracle Exadata, MySQL databases and Oracle enterprise manager dashboard.· Designed and built database structure and objects that best support operational and analytics application.· Experience in Installing and upgrading ops-manager 3.4 for MongoDB and reduced the outages with pro-active monitoring.· Integrated Azure Cosmos DB with machine learning frameworks such as Azure Machine Learning or TensorFlow to train AI models on large-scale datasets stored in Cosmos DB.· 3.4 enterprise edition 3.4 encryption using local key for PII data and worked to pass the Audit for Identity application· Upgraded Enterprise MongoDB 3.4 from community MongoDB 3.0 & 3.2 which provided new functionality and improved performance and helped on audit and backup· Improved the MongoDB performance by reducing too many indexes, rewriting query, improved the database modelling.· Converted data requirements into database structures for optimal performance and availability.· Experience in designing the ETL framework.