Raghav Dixit
Associate Software Development Engineer
Uttar Pradesh, India
Summary
🤩
Rockstar🎓
Top SchoolRaghav Dixit is an Associate Software Development Engineer and Applied AI enthusiast with 7 years of hands-on experience building ML and data-driven solutions while pursuing a B.S. in Data Science at IIT Madras. He specializes in Python, SQL, model development, and visualization tools like Power BI, turning messy real-world data into actionable insights for businesses and research. His open-source contributions include implementing multimodal and Apple-silicon-optimized embedding functions and integrating LanceDB vector storage for retrieval-augmented generation pipelines, reflecting practical expertise in production-grade vector search. Raghav has applied his skills across industry and academia—from optimizing sales and inventory analytics for an FMCG startup to building predictive models for flight pricing and wine quality, and interning on cybersecurity research at IIT Madras. A competitive badminton player, he likens coding to sport: iterative, strategic, and performance-driven, which fuels his continuous learning and problem-solving approach. Based in Uttar Pradesh, he combines strong mathematical foundations with a knack for making complex models accessible to stakeholders.
7 years of coding experience
1 year of employment as a software developer
Indian Institute of Technology MadrasIntermediate, Intermediate at Dr. Virendra Swarup Education CentreBachelor of Science - BSc, Mathematics, Bachelor of Science - BSc, Mathematics at Chatrapati Sahuji Maharaj Kanpur University, Kanpur
English, Hindi
Github Skills (16)
machine-learning10
retrieval-augmented-generation10
vector-database10
approximate-nearest-neighbor-search10
mlmodel10
embedding10
python10
embed10
ml10
llm10
pytest9
testing9
transformers9
audio-processing8
image-processing8
Programming languages (5)
Github contributions (5)
lancedb/lancedb
Jan 2024 - Jul 2024
Developer-friendly OSS embedded retrieval library for multimodal AI. Search More; Manage Less.
Role in this project:
ML Engineer
ML EngineerContributions:13 reviews, 19 PRs, 4 pushes in 5 months
Contributions summary:Raghav contributed to the implementation of a new embedding function using the GTE-MLX model for Apple silicon devices, enabling vector search capabilities within the LanceDB project. They added support for the ImageBind embedding function, providing multi-modal embedding capabilities for images, text, and audio. The user also updated the GTE embedding function's model name and fixed existing bug reports, contributing to the overall functionality and usability of the LanceDB project.
aimultimodalapproximate-nearest-neighbor-searchimage-searchnearest-neighbor-search
llmware-ai/llmware
Jan 2024 - Jan 2024
Unified framework for building enterprise RAG pipelines with small, specialized models
Role in this project:ML Engineer
ML EngineerContributions:1 PR, 1 comment in 2 days
Contributions summary:Raghav's commits primarily focused on integrating and testing vector database functionalities for the `llmware` framework. They implemented support for LanceDB, a vector database, and added associated testing procedures. The commits demonstrate the development of embedding functionalities, specifically within the context of retrieval-augmented generation (RAG) pipelines. Code changes included modifications to configurations, embedding handlers, and example scripts to facilitate the use of LanceDB and ensure the proper creation, search, and deletion of embeddings within the defined vector database structure.
parsingretrieval-augmented-generationagentsgenerative-ai-toolsllamacpp
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