Dyotak Kachare
+91 9167562525
|
dyotak10@gmail.com
|
linkedin.com/in/dyotak
|
github.com/dyo-tak
Education
Vivekanand Education Society's Institute Of Technology (VESIT), Mumbai
Dec. 2021 – May 2025
Bachelor of Engineering in Artificial Intelligence and Data Science with Honours in Blockchain
CGPA: 8.68
Experience
Institutional Shareholder Services - STOXX
July 2025 – Current
Junior Analyst
Developed a proof-of-concept RAG application on Vespa.ai, enabling document ingestion and high-recall retrieval.
Implemented Python scripts to benchmark & compare Vespa.ai and Elasticsearch performance, validating accuracy.
Replaced Google Translate API with an LLM-based translation, implemented glossary feature for translation consistency.
Mercari, Japan
December 2024 – February 2025
Machine Learning Engineer Intern
Worked in the Trust and Safety team on the Item Moderation project.
Worked with a highly imbalanced dataset (10
-6
positive class ratio) to improve model performance.
Enhanced the production LightGBM model, increasing precision from 3% to 5%.
Fine-tuned Japanese BERT and DistilBERT models, further improving precision to 6.5%.
Cere Labs
May 2024 – July 2024
Software Engineer Intern
Developed a parser for html documents that extracts text from html pages to be ingested for chatbot.
Developed a service that extracts html pages related to user query and uploads it to another internal service.
Improved performance of existing website by changing a polling request to a websocket connection.
Projects
AI YouTube Search
| PyTorch, BERT, Streamlit, Pinecone
Created a RAG (Retrieval-Augmented Generation) system for a limited YouTube video dataset.
Embedded video transcripts using sentence BERT and stored embeddings in Pinecone vector database.
Developed a user interface with Streamlit to facilitate user interaction and video search.
Credit Card Fraud Detection
| Python, Flask, React
Developed a machine learning model for detecting fraud in a highly imbalanced credit card transaction dataset.
Implemented SMOTE and undersampling techniques to improve model accuracy.
Created a backend server using Flask and a frontend interface using React.
Technical Skills
Languages:
Python, Java, C, HTML/CSS, JavaScript, SQL
Technologies/Frameworks:
Scikit-learn, PyTorch, TensorFlow, Flask, FastAPI, React, Node, MongoDB, Docker
Tools:
Git, GitHub, MS SQL Server
Roles
AI CoLegion
May 2024 – May 2025
President
Vivekanand Education Society's Institute Of Technology
Achievements
IIIT SriCity National Health Hackathon
Runner-Up
ISTE Invictus
Winner
VJTI Refactor
Participation
Google Developer Student Club - Bit n Build
Participation
Certificates
NPTEL Deep Learning - IIT Ropar
Neural Networks and Deep Learning
NVIDIA Fundamentals of Deep Learning
Applications of AI for Anomaly Detection
Applications of AI for Predictive Maintenance
Data Parallelism: How to Train Deep Learning Models on Multiple GPUs