---
title: "Aniket Patil - Research Intern in India | Tal"
description: "Meet Aniket Patil, ML Engineer | Computer Vision & Medical Imaging · LLM & RAG Systems | AI Research @ IIT Ropar | PyTorch · LangChain · HuggingFace | Kaggle Master based in India."
image: "https://tal.club/meet/aniket-27/share-image"
---

# Aniket Patil on Tal

## Headline

ML Engineer \| Computer Vision & Medical Imaging · LLM & RAG Systems \| AI Research @ IIT Ropar \| PyTorch · LangChain · HuggingFace \| Kaggle Master

## About

I build annotation\-efficient AI that ships in clinical and production environments — not just benchmarks\. At IIT Ropar, I designed a semi\-supervised cardiac MRI segmentation framework that cut boundary error by 50% \(HD95: 10\.62 → 4\.85\) using only 20% labeled data, outperforming fully\-supervised baselines\. The result was a 1\.8M\-parameter model deployable at the edge\. On the LLM side, I built a production RAG pipeline indexing 7,700\+ ArXiv papers at sub\-2s latency with a 94% faithfulness score and under 6% hallucination rate\. My edge: I operate at the intersection of scarce\-label learning and efficient inference — a rare combination in applied medical AI\. Stack: PyTorch · LangChain · HuggingFace · OpenCV · FastAPI · Docker Published: 2× Springer \(skin lesion analysis & augmentation\) Looking for ML Engineer or Computer Vision Engineer roles in healthtech or applied AI — preferably teams where research meets production\. Let's talk: aniketkolte0406@gmail\.com

## Location

India

## Work experience

Research Intern at Indian Institute of Technology, Ropar, Jun 2024 \- Feb 2025 · 9 mos, Rupnagar, • Developed an uncertainty\-guided semi\-supervised framework, reducing cardiac MRI boundary error by over 50%\. • Engineered a comprehensive PyTorch pipeline, cutting model development cycles by 40% for efficient deployment\. • Validated the effectiveness of annotation\-efficient deep learning, demonstrating superior performance on limited labeled data\.

Research Intern at Vishwakarma Institute of Information Technology, May 2023 \- Aug 2023 · 4 mos, Pune, Maharashtra, India, • Redesigned a SegNet encoder\-decoder CNN, improving skin lesion segmentation by \+7\.38 pp Jaccard and \+5\.30 pp Dice\. • Optimized architecture depth for edge deployment, enabling resource\-efficient clinical inference validated across dermoscopic images\. • Contributed to two peer\-reviewed publications on AI\-based augmentation techniques for dermatology datasets at Vishwakarma Institute of Information Technology\.

## Education

Bachelor of Technology \- BTech, Artificial Intelligence and Data Science, Vishwakarma Institute of information technology

[View the original profile](https://tal.club/meet/aniket-27)

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