---
title: "Kartik Kuppam - Lead Data Scientist in Bengaluru | Tal"
description: "Meet Kartik Kuppam, Lead Data Scientist | Samsung R&D | Ex-Bosch | 9+years | Multi-Model | Computer Vision | NLP/Gen AI | DL | ML based in Bengaluru. AI stack includes gemini, langchain, and langgraph. Explore public work links including github."
image: "https://tal.club/meet/kartik-kuppam/share-image"
---

# Kartik Kuppam on Tal

## Headline

Lead Data Scientist \| Samsung R&D \| Ex\-Bosch \| 9\+years \| Multi\-Model \| Computer Vision \| NLP/Gen AI \| DL \| ML

## About

As a Lead Data Scientist at the Bosch Research, my role revolves around pioneering AI\-driven solutions in manufacturing and engineering\. Our team has successfully delivered projects that reduce labeling and training costs by up to 40%, thanks to innovative foundation models and self\-supervised learning techniques\. In collaboration, we have fine\-tuned large language models for enhanced image retrieval, achieving a ROUGE\-L score of 95% and realizing annual savings of €1M\. Our latest triumph involved optimizing defect classification using state\-of\-the\-art models like ResNet\-50 and CLIP, combined with knowledge distillation, to tackle class imbalance and cut inspection costs by €300K annually\. With my expertise in generative AI, convolutional neural networks, and deployment tools like ONNX and TorchServe, I am committed to pushing the boundaries of AI application and contributing to impactful, cost\-effective solutions in the industry\.

## Location

Bengaluru

## AI stack

gemini

langchain

langgraph

openai

## Work experience

Lead Data Scientist at Samsung R&D Institute India \- Bangalore, Aug 2025 \- Present · 9 mos, Bengaluru, Karnataka, India

Lead Data Scientist at Bosch Research, Jul 2023 \- Jul 2025 · 2 yrs 1 mo, LLM\-Driven Image Captioning & Retrieval for Autonomous Driving Deployed a LoRA\-tuned CLIP system for text\-to\-image retrieval in plant environments, cutting inspection time and driving €1M\+ in productivity gains\. Constructed a domain\-specific dataset by pairing segmentation metadata with contextual captions, incorporating 256\-token expansion, hard negative mining, and angular margin loss for contrastive learning\. Replaced GPT\-based generation with fine\-tuned LLaMA\-1B/7B models using LoRA and QLoRA, boosting ROUGE\-L by 15% and reducing GenAI inference costs by 80%\+\. AskBosch: Agentic RAG Chatbot with GPT\-4o Designed and deployed an agentic Retrieval\-Augmented Generation \(RAG\) system to process 2TB\+ of Bosch internal documents\. Built a scalable backend with Azure Functions \+ Databricks and an interactive Streamlit front\-end, enabling internal rollout via the AskBosch GenAI suite—projected to save €4M annually for 1,000\+ users\. Enhanced document ranking using multi\-query expansion, dense\+BM25 hybrid retrieval, Reciprocal Rank Fusion, and bi\-encoder re\-ranking\. Used LLaMA 3\.2\-11B on Databricks for structured insight extraction\.

Senior Data Scientist at Bosch Research, Jan 2021 \- Jul 2023 · 2 yrs 7 mos, AOI Studio – Foundation Models for Manufacturing Developed self\-supervised foundation models using ViT and ResNet\-50, leveraging SimCLR and DINO for feature learning on unlabeled manufacturing data\. Achieved 90% accuracy with just 20–30% labeled data—outperforming transfer learning baselines with 60–70% less supervision\. Enabled €1M in savings through reduced annotation effort and faster deployment\. Integrated into AOI Studio, reducing labeling and training costs by 30–40%, and scaling across multiple Bosch manufacturing use cases\. Applied knowledge distillation and diffusion\-based generative models \(DDPM, DDIM\) to improve visual inspection robustness\. Regulation Document Classification Built a scalable NLP pipeline for long document classification using BERT and DistilBERT, automating compliance triage for over 60K regulatory docs across 4 Bosch units\. Engineered metadata\-based features and optimized for 99% recall and 63% precision, addressing class imbalance and label ambiguity\. Delivered €10M\+ annual cost avoidance via automation\.

Data Scientist at Bosch Center for Artificial Intelligence \(BCAI\), Jul 2019 \- Jan 2021 · 1 yr 7 mos, Bengaluru, Karnataka, India, Developed and deployed AI\-powered defect detection and segmentation models \(YOLOv3, YOLOv4, U\-Net\) with 150ms latency, optimizing accuracy and real\-time performance in manufacturing\. Optimized and converted models to ONNX, deploying via TorchServe on WSL, reducing inspection costs by €300K annually\. Designed a radar data generation pipeline, simulating real\-world sensor data using PointNet, PointPillars, and PointCapsuleNet\. Trained Autoencoders \(AE\) and Variational Autoencoders \(VAE\) for point cloud data synthesis, improving anomaly detection using MSE and Mahalanobis Distance\.

Scientist\-B at DRDO, Bangalore, Jun 2017 \- Jun 2019 · 2 yrs 1 mo, Bengaluru, Karnataka, India, Designed a real\-time system using Conv3D, MobileNet, and RNN, trained on custom video datasets for display control in tactical applications\. Developed an NLP\-driven pipeline for classifying technical documents and enabling SQL\-based audit trails, improving traceability and cutting manual review time by 60%\.

Project Engineer at Indian Oil Corporation Limited, Oct 2015 \- May 2017 · 1 yr 8 mos, Bhubaneshwar, Odisha, India

## Education

Master's degree, Artificial Intelligence, Liverpool John Moores University

PG Diploma, Artificial Intelligence, International Institute of Information Technology Bangalore

## Public links

- github: https://github\.com/kuppamk

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

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