Senior Software Engineer at FIS Global Solutions Inc with expertise in Python and PySpark. Proven ability to design robust data pipelines and enhance analytics capabilities. Experienced in predictive modeling, classification, and exploratory analysis to derive actionable insights. Proficient in managing end-to-end data workflows, including ingestion, transformation, visualization, and machine learning.
Client - Royal Bank of Canada (RBC)
Client - Bank of Montreal (BMO)
Client – Warner Music Group.
Client - Carnival Cruise Lines (CCL)
Client - Income tax of India (ITAX)
Music Recommendation System (Great Learning ePortfolio)
Combine the user-user collaborative filtering model with the item-item collaborative filtering model to recommend songs based on the listening behavior of similar users as well as songs that are similar to ones that users have listened to in the past. This hybrid approach can potentially provide a good balance between the desire for personalized recommendations and the desire to recommend songs that are popular among similar users.
Amazon Product Recommendation System (Great Learning ePortfolio)
This project involves recommending the best Amazon products available to users based on past rating data using AI-driven recommendation systems techniques.
Stock Data Pipeline (GitHub)
- Developed a streaming pipeline using Spark, Kafka, and Delta Lake on Azure Databricks.
- Ingested and transformed real-time trade/quote data using PySpark.
- Orchestrated workflows via Apache Airflow
Citizenship: Canadian Citizen