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chakshu-dhannawat/README.md

Chakshu Dhannawat

AI Engineer · Otsuka Corporation · Tokyo

I build production LLM systems: RAG pipelines, LLM fine-tuning, and agentic workflows at scale. Currently deploying AI infrastructure on the ELK stack and running LLM training across multi-node on-prem GPUs at Otsuka Corporation, Tokyo.

B.Tech in AI & Data Science (Computer Vision specialization) from IIT Jodhpur.


What I work on

  • RAG & Search: production RAG chatbots on Elasticsearch (vector + hybrid search), 1,000+ monthly users, 31% retrieval accuracy improvement
  • LLM Fine-tuning: SFT, instruction tuning, RLHF on 10+ LLMs using LoRA/QLoRA across multi-node GPU clusters
  • LLM Serving: vLLM-based inference, multi-agent orchestration (memory + planning), automated 60% of routine enterprise queries
  • Agentic Systems: LangChain, LlamaIndex, AutoGen, CrewAI; multimodal LLM pipelines, tool use, evaluation harnesses

Tech stack

Python PyTorch TensorFlow Hugging Face LangChain Elasticsearch vLLM Docker Kubernetes AWS Linux Git


Connect

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  1. Retinal-Image-Analysis Retinal-Image-Analysis Public

    Image Segmentation and Classification for Medical Applications

    Jupyter Notebook 6

  2. GenerativeAgents GenerativeAgents Public

    LLM-powered multi-agent simulation of Werewolves of Miller's Hollow using GPT and memory-based social deduction

    Python 2

  3. Data-Augmentor Data-Augmentor Public

    Streamlit UI for augmenting SFT and alignment datasets via LLM — supports multi-turn QA, JSONL/CSV, live progress, and configurable concurrency

    Python