As a dedicated Data Scientist with over 6 years of experience, I have honed my expertise in both traditional machine learning techniques and cutting-edge natural language processing (NLP). My career journey began with a strong focus on conventional machine learning, where I developed foundational skills in building scalable, end-to-end systems. Over time, I have expanded my scope to include advanced technologies, culminating in impactful contributions to state-of-the-art Retrieval-Augmented Generation (RAG) systems.

Currently, I am a part of the innovative team at DataWorkz, driving enterprise-level RAG implementations. My work includes:

Building and enhancing RAG pipelines to deliver scalable and accurate results. Developing Agentic RAG systems that integrate dynamic tool capabilities for complex use cases. Leading the development of GraphRAG, combining graph-based approaches with RAG to unlock new possibilities for enterprise applications. Finetuned small language models on domain specific tasks.

📊 Key Achievements:

Designed a robust metric system for evaluating and comparing different RAG pipelines, enabling data-driven decision-making for system improvements. Integrated tools into Agentic RAG systems to expand functionality and efficiency. Spearheaded the creation of an enterprise search engine powered by Locality Sensitive Hashing (LSH) for fast and effective information retrieval. Built custom solutions using advanced algorithms like HDBSCAN, multi-class text classification with logistic regression, and custom NER detectors in early-stage projects. I thrive on solving complex data problems and enjoy bringing clarity to unstructured data through innovative solutions. My passion for machine learning and AI drives me to continuously explore and implement cutting-edge technologies to meet real-world challenges.

Checkout theirLinkedIn Profile!