Hire Kousalya - RAG Engineer

Designed and implemented a RAG-based conversational chatbot using Python and NLP techniques. Used embedding models to convert documents into vector representations for semantic search. Stored and retrieved vectors using embeddings to support accurate context retrieval. Integrated retrieval results with a generative model to produce contextual responses. Exposed chatbot functionality through REST APIs using Flask.

I worked as a Software Developer where I was mainly involved in building and improving backend systems for SaaS-based products. One of my key contributions was working on an intelligent search solution that combined semantic search with structured filtering, helping improve how users retrieved data across applications. I worked closely with AWS services like Lambda and API Gateway to build scalable serverless APIs, and used OpenSearch and DocumentDB to manage search data, indexing, and activity logs. I also focused on securing APIs through token-based authentication and ensuring the system handled multiple applications efficiently. Alongside this, I contributed to enhancing a marketing automation product used by global clients. I developed frontend features and backend services to support user authentication, workflow processes, and approval systems. Working in an Agile setup, I collaborated regularly with QA, UX, and Product teams to deliver features smoothly and on schedule. This experience strengthened my understanding of scalable architecture, search optimization, and production-level application development.