Kowsalya Govindaraj

I’m a Generative AI Engineer with around 3 years of experience designing, developing, and deploying enterprise-grade AI applications using Large Language Models (LLMs), Retrieval Augmented Generation (RAG), embeddings, and vector databases. I focus on building secure, controlled AI responses that stay accurate and contextually relevant.

In my roles at Tata Consultancy Services and Nagarro, I integrated LLM services into enterprise RAG pipelines, built semantic search and document retrieval systems using FAISS and embedding models, and developed scalable backend APIs and AI orchestration workflows with AWS services. I also worked on AI response validation, logging, monitoring, and retrieval optimization to improve production reliability.

I’m strong in prompt engineering, prompt optimization, and response tuning, and I’ve implemented RAG pipelines with LlamaIndex, LangChain, embeddings, and vector databases like Pinecone and FAISS. I’ve built chatbot systems, document question-answering platforms, enterprise search solutions, and knowledge assistant applications.

I work end-to-end with multiple data sources—including PDFs, CSV files, relational databases, APIs, and cloud storage—and I optimize LLM inference costs, reduce latency, and improve response accuracy through efficient retrieval strategies. My approach is practical and measurable: build, validate, monitor, and iterate until the system performs in real enterprise settings.