• Leveraged generative AI with Vespa for advanced profile searches, successfully implementing the system to enhance candidate identification and matching processes.

Machine Learning Engineer•Successfully deployed ML models as batch endpoints on Azure ML and Databricks platforms, ensuring seamless integration and efficient data processing.•Developed robust deployment pipelines using MLOps principles, enabling automated model deployment, monitoring, and scaling, resulting in improved productivity and reduced time-to-market.•Implemented continuous integration and continuous deployment (CI/CD) practices for machine learning models, ensuring rapid and reliable deployment of new model versions and updates.Data Engineer• Designed an end-to-end architecture for a client on the Azure cloud, facilitating the migration of their on-premises data to Azure Data Cloud.• Developed several ingestion pipelines on Azure Data Factory and Databricks notebooks to support their business logic.• Implemented a CI/CD pipeline for Azure Data Factory utilizing GitHub Actions to deploy code changes to higher environments.

• Collaborated with the Snowflake team and assisted multiple customers in migrating/onboarding to the Snowflake data platform.• Designed and maintained over 60 dynamic data ingestion pipelines connecting various on-premise source systems (SQL Server, SFTP Server, S3 Bucket, MySQL, Db2, PostgreSQL, etc.) with target systems (Azure Data Lake Storage, Snowflake, Azure Synapse Analytics) using Azure Data Factory.• Created Azure Databricks notebooks for Extract, Transform, and Load processes for multiple source and target systems.

• Worked on creating PowerBI dashboards for the insurance company, specifically focused on claims, vendor agency, and agent data.• Utilized Snowflake as the data warehouse and Power Apps for alerts and write-back functionality.• Developed a Python-based utility tool for migrating data between on-premises sources such as SQL Server, MySQL, PostgreSQL, and Snowflake.