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Sanket Muchhala

Sanket Muchhala

AI/ML engineer specializing in generative AI, LLMs, and NLP

Bloomington, Indiana, United States
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Joined April 2026
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Network

6.7K connections
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Indiana University Network
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Amazon AWS Engineers

Summary

Generative AI and LLM engineering: Sanket focuses on building agentic systems, RAG pipelines, and LLM-driven document intelligence workflows—both in research and production—demonstrated by portfolio projects and research-assistant work. github+1
Production-grade ML for enterprise: He has engineered end-to-end ML solutions in industry settings (Progressive, IBM), including model training, CI/CD deployment to Azure ML, containerization on Kubernetes, and observable pipelines that delivered measurable business impact. github+1
Research and technical writing: While studying at Indiana University and earlier at Thakur College, he contributed to applied ML research and conference papers and maintains technical writing on AI topics. google+2
Open-source tooling and product mindset: Sanket publishes projects and repositories (e.g., Nerdplexity / Lantern) that blend product-focused analytics with local-first privacy and performance considerations for AI experimentation. github+1

Work

Education

Projects

Writing

AI vs Human Brain

August 1, 2025

A long-form blog exploring the conceptual and philosophical differences between artificial intelligence and human cognition, arguing that AI is a set of mathematical algorithms and discussing implications for how humans should use AI.

Favicon imagesanketmuchhala.github.io

Disease Prediction using ML

January 1, 2021

Conference/journal paper describing supervised machine-learning approaches for early-stage disease detection, reporting comparative model accuracy and proposing a multimodal approach with reported high accuracy.

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Health-Bot (Healthcare Chatbot) (conference contribution)

January 1, 2020

A student-authored project/paper on a healthcare chatbot presented in an e-conference on AI/ML.

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Hobbies

Writes and blogs about AI, philosophy, and the implications of generative models. github+1