At Tinvio, we are pioneering the next-gen accounting and finance platform built for the GPT era.In my role, I focus on utilizing Python and Go, along with GraphQL, to power our user interface and generate detailed Excel financial reports. Additionally, I leverage AWS to parse and process financial documents, ensuring high performance and scalability.

-Key contributor to the Order Assignment Service, the microservice responsible for seamlessly delivering optimized driver assignments for orders across ~1200 cities, operating concurrently with a 30-second interval.-Reduced Delivery Time breaches in inclement weather by ~2 minutes to improve user experience and order assignment by designing a Realtime Driver Speed signal that responds to environment changes instantly-Reduced Driver Wait times to improve operational efficiency by implementing time correction features leveraging historical correction factors obtained via Apache Kafka from Python Jobs triggered by scheduled Airflow ETLs-Enhanced system efficiency by migrating critical API-driven processes to streamlined event-based workflow with MongoDB as primary storage, mitigating the impact of downtimes on external servicesBoosted accuracy by ~5%, improved driver utilization by 0.3 minutes by integrating in-memory Light GBM ML models for distance and time prediction that are fetched on-demand from AWS S3, replacing external gRPC calls-Enhanced system reliability by eliminating shared DB queries from the service, migrating them to HTTP calls, and implementing Redis fallbacks to handle failures.-Substantially contributed to the development of Logistics Simulation Service, a robust load testing framework to comprehensively test the entire Logistics Delivery Flow-Set up the entire data pipeline flow to synchronize and update all relevant SQL and NoSQL databases for simulating and analyzing behavior under various load conditions.-Implemented mock gRPC servers for essential external services and integrated high-throughput critical App APIs, achieving a peak throughput of ~100k rpm-Achieved 10x the highest traffic ever recorded in production by enhancing system's capability to replicate production-like traffic peaks

-Achieved an accuracy score of 0.82 by implementing a DL model for semantic segmentation of engine point cloud using PointNet Architecture; augmented the data using multiple orientations to overcome the lack of data-Automated Finite Element Analysis pre- and post-processing steps by building a multipurpose tool using Python. Delivered a standalone executable file of the tool using PyInstaller to cater to industry requirements.