I have led the entire backend and managed a team of 3 software engineers to build the backend using FastAPI with PostgreSQL. All deployments were handled using GCP services like GKE, GCP Cloud Storage, and more.

I have built the backend for an LLM-powered chatbot application using Django. The chatbot effectively answers users questions based on their uploaded PDFs, leveraging AWS Bedrock Claude models, with PostgreSQL as the database. In development we used AWS CodeCommit for version control and AWS S3 for file storage, all seamlessly deployed through AWS Pipelines.

I contributed to an internal team, focusing on the development of management tools. My responsibilities included utilizing FastAPI, implementing PostgreSQL with SQL Alchemy, and integrating Azure BLOB for storage purposes. And also used Azure Repos for version control and documented those APIs using SwaggerUI and Postman.

Implemented an ETL pipeline to extract metadata from 25+ data warehouses that were stored in Redshift, Hive Metastore and data catalog platform like Alation, streamlining the process of loading data into PostgreSQL for the creation of a Data Dictionary. This significantly reduced the effort required for manual creation.

Gathered technical requirements from clients, evaluated solutions for potential bottlenecks, technical debt, and future scalability, and designed overall projects. Subsequently, conveyed the requirements to the development team, assisting them in establishing pipelines for data processing, model training, API building, and deployment.

Python Developer and ML Engineer

Built backend APIs using Python and Flask, interacting with SQL databases such as MySQL, PostgreSQL, and AWS RDS Oracle, as well as NoSQL databases like MongoDB. Dcokerize and deployed on AWS EC2 instance.

Built a data augmentation pipeline for an Indian double-lined two-wheeler's license plate image dataset. This pipeline involves taking an empty number plate image with some tilt and masking it with a desired number PNG image to create a fake license plate dataset using Python and OpenCV.

Built an ANPR (Automatic Number Plate Recognition) PoC for a Honda service center. The system detects and recognizes the vehicle type and license plate, handling all types of license plates (single-lined and double-lined). YOLO V3 is employed for vehicle type detection, YOLO Tiny for license plate detection, and a CNN for OCR. The extracted data is then stored in MongoDB.

I built a CMS web app for the Smart Campus project, where employees can register and later be assigned to specific buildings and floors for management, including contact information.

CMS system was built using Django with Bootstrap as the frontend and MySQL as the database. The management of all these components was handled with the XAMPP server, and sessions and password hashing were implemented for authentication.