Dr. Rajesh More

The Hartford

AI Engineer

June 13, 2026 – Present

Pulse-WebQA_Agent

December 18, 2024 – December 19, 2024

A powerful documentation QA system that crawls help websites, processes content, and provides accurate answers using RAG (Retrieval-Augmented Generation) with Google's Gemini AI.

MLOPs-Specialization

July 1, 2023 – July 1, 2023

MLOPs-Specialization — GitHub repository

Statmike-Vertex-AI-Repo

December 12, 2022 – December 12, 2022

https://github.com/statmike/vertex-ai-mlops.git

GCP-Certification-Professional-Machine-Learning-Engineer

September 12, 2022 – November 8, 2022

Notes For Reference

Natural-Language-Processing-Course-

August 8, 2022 – October 20, 2022

Author: Rajesh More

DataScience_Mentorship

March 26, 2022 – Present

Course Material - Data Science Program

Semiconductor-manufacturing-process

October 9, 2021 – November 14, 2021

CONTEXT: A complex modern semiconductor manufacturing process is normally under constant surveillance via the monitoring of signals/ variables collected from sensors and or process measurement points. However, not all of these signals are equally valuable in a specific monitoring system. The measured signals contain a combination of useful information, irrelevant information as well as noise. Engineers typically have a much larger number of signals than are actually required. If we consider each type of signal as a feature, then feature selection may be applied to identify the most relevant signals. The Process Engineers may then use these signals to determine key factors contributing to yield excursions downstream in the process. This will enable an increase in process throughput, decreased time to learning and reduce the per unit production costs. These signals can be used as features to predict the yield type. And by analysing and trying out different combinations of features, essen

Loan-Default-Prediction

February 10, 2021 – February 10, 2021

Numerous companies from financial indutry often invest considerable resources to improve their predictive models with the aim of having better insights into their customers. Such an interest in model improvement has intensified in recent years mostly because of fast development of machine learning and artificial intelligence. For standard lending institution default predictive model with high performance helps to considerably minimize Credit Loss, resulting in higher revenue and profits. Usually the better predictive model the more efficient is the underwriting policy and collection process. A well-functioning model should distinguish creditworthy customers from those that are credit risks. Often, more-predictive credit-decisioning model can identify a greater number of customers within an institution’s specified risk tolerance, which should expand revenues as well. In this project the goal is to increase detection of defaulted loans before the loan is issued/offered by P2P lending com

NLP-basics-Theory-

January 21, 2021 – January 21, 2021

Tags: text cleaning, stemming, lemmatization, vectorization, stopwards, removing punctuations, hashtags, links etc.

Capstone-Project-2

January 4, 2021 – June 8, 2021

Corona Virus Sentiment Analysis.This challenge asks you to build a classification model to predict the sentiment of COVID-19 tweets.The tweets have been pulled from Twitter and manual tagging has been done then.