Divi Eswar Chowdary

Currently Playing with LLMs

Andhra Pradesh, India

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About

As an AI Engineer, I specialize at converting ideas into deployable models. I am a Computer Science student specializing in Artificial Intelligence, with a strong academic background. I am particularly interested in computer vision, natural language processing (NLP), and generative AI. I enjoy working on efficient deep learning models and have developed innovative AI solutions throughout my academic career.

Work Experience

Schlumberger
Onsite

2024 July - Present

Data Scientist

  • Contributes to the Digital Factory AI for Safety & Operations platform, improving safety, compliance, and efficiency across 30+ locations and 800+ cameras.
  • Developed and optimized computer vision models for hard hat detection, coverall detection, and real-time forklift proximity monitoring.
  • Enhanced mechanical lifting compliance detection, improving alert accuracy from 70% to 90% by introducing a depth-estimation model and upgrading to VideoMAE video classification.
  • Built the "People Finder - AI Knowledge Agent," an AI chatbot for employee queries using an internal People Database.
  • Used LangChain and LangGraph for agentic workflows and implemented hybrid retrieval pipelines using SQL-based filters and vector embeddings.

Schlumberger
Onsite

2023 June - 2023 August

Data Scientist Intern

  • Built deep learning-based product embeddings for Supply Chain Intelligence to recommend alternatives and support decision-making.
  • Designed a multi-task learning architecture using RFM attributes and product metadata to generate unified embeddings.

Education

Amrita Vishwa Vidyapeetham

2024 - 2024
Bachelor's Degree in Computer Science with Artificial Intelligence

Skills

Deep Learning: PyTorch, Lightning AI, Transformers
LLMs: Langchain, Llamaindex, Langgraph
Databases: MongoDB, Supabase, Pinecone
Languages: Python,JavaScript, SQL
Frameworks: Streamlit, FastAPI, Gradio

Projects

ModelHub

ModelHub is a platform for sharing, discovering, and running machine learning models

Full Stack Developer
JavaScript
React
Node.js
Python

Podcastify

Convert a convert articles from URLs into listenable audio Podcasts.

Python
LLMs
Streamlit

ChatwithPDF

A platform to chat with PDFs

Python
Chromadb
LLMs
Streamlit

MedGPT: GPT for Medical Diagnostics

Developed a medical chatbot by fine-tuning the LLama 2 model using efficient fine-tuning methods. Additionally, incorporated the Retrieval Augmented Generation (RAG) to enhance the output quality of generated responses. For this project, dataset was created by translating an existing Korean dataset into English.

Python
Llama 2
RAG
Fine-tuning

RosBot: Self-Driven Robot with Obstacle Avoidance

Designed and implemented RosBot, a self-driving robot with obstacle avoidance, using ROS, TensorRT, Resnet18, and OpenCV. Optimized Resnet18 model with TensorRT for accurate obstacle detection and utilized OpenCV for efficient image processing.

ROS
TensorRT
Resnet18
OpenCV

Research Papers

An analysis of data leakage and generalizability in MRI based classification of Parkinson's Disease using explainable 2D Convolutional Neural Networks

2024
This paper presents a study on the detection of Parkinson's Disease (PD) from T1-weighted MRI scans using Convolutional Neural Networks (CNNs). The study investigates the potential for bias propagation in CNN models due to data leakage and evaluates the generalizability of the models to external datasets.

Transformer-Based Multilingual Automatic Speech Recognition (ASR) Model for Dravidian Languages

2024
This paper presents a approach to ASR for Dravidian languages like Tamil and Telugu. It builds upon the strengths of the powerful Whisper model, known for its multilingual capabilities, and fine-tunes it specifically for these under-resourced languages. This approach achieves significant improvements in WER compared to existing models.

Going Beyond Traditional Methods: Using LSTM Networks to Predict Rainfall in Kerala.

2024
This paper presents an approach to forecast rainfall in Kerala using LSTM models. The study compares the performance of LSTM models with traditional time series forecasting methods and demonstrates the superior accuracy of LSTM models in predicting rainfall in the region.