---
title: "Shreeyash Pawar - Applied Scientist in Bengaluru | Tal"
description: "Meet Shreeyash Pawar, ML @ Philips Research | 2 Patents | Ex Société Générale | COEP | 5x AI hackathons wins based in Bengaluru. AI stack includes codex, github copilot, and hugging face. Explore public work links including github."
image: "https://tal.club/meet/shreeyash-pawar/share-image"
---

# Shreeyash Pawar on Tal

## Headline

ML @ Philips Research \| 2 Patents \| Ex Société Générale \| COEP \| 5x AI hackathons wins

## About

Discovering the world, one dataset at a time\. In constant search of finding and solving hard problems\. Now dabbing into "applied" ML world at Philips\. Previously tapped into world of deep learning for life sciences as 1st employee at VC Funded Sravathi\.ai\. Github: https://github\.com/shree970/

## Location

Bengaluru

## AI stack

codex

github copilot

hugging face

langgraph

ollama

openclaw

## Work experience

Applied Scientist at Philips, Oct 2024 \- Present · 2 yrs, Crafting GenAI solutions from research to production\. \- Integrated VLM's \(Qwen\-omni\) for video ingestion in patient monitoring chatbot \- Computer use agent \(CUA\) for zero shot navigation of medical screens and courses \- Scaling test time inference in agentic text2sql tasks

Research Engineer II at Philips, Dec 2021 \- Oct 2024 · 2 yrs 11 mos, • Led a 4 people team that built an full\-stack end\-to\-end AI vision annotation platform, cutting clinical imaging annotation time by more than 4× Stack → Pytorch \| AWS \| EKS \| FastAPI \| SQLAlchemy \| Docker \| IaC/CloudFormation \| Airflow • Trained a model that reconciles many human annotations into one ground truth for medical segmentation, using statistical methods & deep learning \(Probabilistic UNet\) Stack \-> Pytorch \| OpenCV \| 3D Slicer \| ITKSNap \| Numpy \| FastAPI • Brought deep\-learning to the browser for real\-time, server\-less insight at the edge for a X\-ray imaging platform\. Stack → C\+\+ \| ONNX \| TensorFlow\.js \| SIMD\-powered WASM • Gave clinicians back hours each week through a near zero\-hallucination clinical text summarizer, stress\-tested against human experts\. Stack → LangChain \| LlamaIndex \| Azure \| statsmodels \| DSPy \| RAG • Developed and benchmarked knowledge graph for document search and categorization\. Stack \-> AWS Neptune \| Neo4j \| NetworkX \| Gremlin

Data Scientist at Sravathi AI Technology Pvt Ltd, Apr 2020 \- Dec 2021 · 1 yr 9 mos, Bengaluru, Karnataka, India, Applied deep learning in life sciences domain\. Project: Optimizing Drug Formulations Using AI • Developed an application that determines optimal concentration values for drug formulations using SciPy's minimize solver\. • Enhanced ML prediction pipeline performance by 90% through code profiling and optimization using JobLib parallel processing, Numba JIT compilation, cuML, cuDF, and multiprocessing\. • Built end\-to\-end Python application with robust data pipelines using DVC for data ingestion, MLflow for model tracking, Dask and H2O AutoML for training, GitLab CI/CD pipelines for deployment, and Docker registry for model serving\. Project: De Novo Molecule Generation • Implemented generative modeling using unsupervised/semi\-supervised deep learning methods \(GANs, VAEs\) and encoder\-decoder models for novel drug discovery targeting specific proteins\. • Work resulted in 1 patent filing\. Infra: Set up AI training infrastructure with Ubuntu Server, configured GitLab Enterprise Server, standardized deployment pipelines, and provided Linux environment troubleshooting\.

SDE\-1 at Societe Generale Global Solution Centre, Jul 2019 \- Apr 2020 · 9 mo, Greater Bengaluru Area, Python programming for internal tools

Intern at IoTIoT\.in, Jun 2018 \- Jul 2019 · 1 y 1 mo, Pune, Maharashtra, India, Worked on Computer Vision for Embedded Linux platforms\. • Developed real time scalable face recognition system that can run on constrained ARM devices\. • Successfully piloted “AI Face registration” at “Women in DS, conference’19”,a Stanford initiative, at Pune\. • Achieved record FPS on Arm v7 architecture using model optimisation techniques and scrutinized the effect of various tuning parameters on a standard dense neural network

Electrical System Head at Team Velociracers, Jan 2017 \- May 2018 · 1 y 4 mo, India, The team builds a hybrid trike, mechanically operated by pedaling and electrically driven by a motor, powered by a battery\. I lead electrical and electronics subsystem\.

## Education

Bachelor of Technology \- BTech, Electrical, Electronics and Communications Engineering, COEP Technological University

## Public links

- github: https://github\.com/shree970

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

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