Nakul Singh

Led a team of 7 engineers, owning technical execution, sprint planning, delivery tracking, and code/design reviews for customer-facing features. Built a GenAI RAG system that reduced manual parking appeal review time by 65% and improved auditor alignment to 94%, and engineered an async bulk ingestion engine processing up to 1M records per batch to cut manual operational labor by 30 hours weekly.

Established CI/CD pipelines with GitHub Actions and infrastructure-as-code to reduce deployment time by 80% and eliminate manual release errors. Architected a scalable application-renewal automation system that cut processing from 10–12 days to 1 day, and built a domain-matching algorithm that reduced customer support tickets by 18%.

Designed a parallelized report generation architecture that decomposed long-running workflows into asynchronous tasks, reducing processing time by 90%. Introduced Apache Solr search for large datasets to improve query response times by 80% and led migration of a core external data provider, increasing ingestion success rates by 75%.

Implemented backend services to ingest and normalize data from heterogeneous sources, processing 2M events per day into a unified domain model. Validated scalability using load and stress testing, ensuring reliability under peak traffic (2,000 RPS) with Locust and GCP Monitoring.

Developed an image processing and OCR pipeline using OpenCV to extract structured text from images, automating data capture and reducing manual processing effort by 90%.

Grade: GPA: 8.85/10

Bachelor of Technology degree with a GPA of 8.85/10.