Data & ML Engineer with extensive experience in building scalable data platforms and implementing machine learning solutions. Currently focused on developing data-driven solutions for business intelligence and analytics.
Technologies: Python, SQL, Azure Synapse, ADF, RAGs, LLMs, Flask, Azure Fabric
• Designed and developed scalable data platforms to meet evolving processing requirements
• Implemented innovative solutions using Large Language Models (LLMs) to generate actionable business insights
• Supported key business initiatives such as Windows Passkey and Recoverability, driving metrics and OKRs to enhance the Windows user experience
• Explored advanced AI technologies including LLMs and Retrieval-Augmented Generation (RAG) for comprehensive Business Intelligence solutions
• Collaborated with cross-functional teams to integrate emerging technologies and optimize data workflows
Technologies: SQL, Python, Bash, SCOPE, Azure ML , Azure Synapse
• Windows & Devices: Developed critical business metrics for MAD and DAD calculations, and established performance indicators and OKRs
• Azure Services: Successfully migrated legacy systems to Azure using Synapse Analytics and Data Factory
• Store Personalization: Reduced machine learning model training costs by 80% through the implementation of the Resilience LPG framework
• Enhanced system scalability and performance by integrating agile methodologies and streamlined data pipelines
Technologies: Python, Bash, Hive SQL, JS
• Fintech and Tax: Enhanced and built data pipelines for monthly/weekly compliance reports
• Internal Tooling: Developed an ETL product with modeling, report generation, and orchestration capabilities
• Created self-serving tools for Tax audit teams
Technologies: SQL, Python, Bash
• Architecture Design: Designed scalable solutions using Amazon ECS, Fargate, EC2, and S3
• Data Ingestion: Built critical datasets using Redshift for Amazon Logistics (AMZL)
• Improved SLA by 25% and enhanced data quality through pipeline optimization
• Built DEA (Delivery Estimate Accuracy) tracking systems for Indian Logistics
Technologies: SQL, Python, Bash, Hadoop
• Developed Cost-Based Allocation Model for Chase Merchant Services
• Built Tableau Dashboards with 300+ daily active users for Mortgage Banking
• Achieved 50% SLA improvement through Spark SQL optimization
• Implemented CI/CD using Jenkins and BitBucket
Technologies: Python, R
• Developed algorithms for vehicle fault detection in engines
• Implemented text mining techniques for data filtering
• Created ground truth verification systems
Degree: Completed Dual Degree in Electrical Engineering (Minor in Development Policies)
Duration: 2012 – 2017
The project was carried out on campus in collaboration with the college research labs, focusing on Vehicle Classification using Machine Learning and Dynamic Time Warping.
View Thesis PDF