• Redesigned database and REST API endpoints for consumer facing platforms using PostgreSQL and Express to increase system reliability and speed of content delivery.

• Developed a unified PostgreSQL data warehouse to integrate multiple data sources, reducing data redundancy and streamlining customer outreach, which contributed to a 10% increase in user retention.

• Built automated data monitoring and reporting pipelines to track customer engagement metrics, delivering actionable insights that drove a 15% increase in feature adoption.

• Reduced MRI scan time by 50% by designing and train Deep Learning models using TensorFlow and Python scripts.

• Designed ETL pipelines using Python and Apache Airflow, processing large-scale healthcare data, resulting in reduced processing times of 30%.

• Developed customer facing dashboards across the stack using React and Postgres to automate report generation and data ingestion.

• Created a React component library for visualizing KPI and metrics using D3.js and GraphQL wrappers.

• Revamped PostgreSQL databases increasing efficiency, reducing query times by 50% and ensuring fast, on-demand retrieval of high-volume datasets for analytics and reporting.

• Led the integration of ML based SKU level planogram compliance reports for 2000+ supermarkets into client facing React dashboards.

• Trained and deployed on-device ML models for mobile devices using a React frontend for sub 0.5s image search of supermarket items.

• Migrated ML applications to serverless architecture using AWS EKS (Kubernetes), AWS EFS and

RabbitMQ queues to reduce GPU usage and save upto $200,000 per year.