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Lead Machine Learning Engineer @ Wizard AI | PhD, Computer Science, Machine Learning, Natural Language Processing
As a Lead Machine Learning Engineer at Wizard AI, I focus on designing and implementing advanced AI-driven systems that address complex challenges in data processing, product classification, and recommendation optimization. My expertise spans machine learning, natural language processing, and multimodal AI, with an emphasis on developing scalable, high-impact solutions that integrate seamlessly into real-world applications Key Highlights of My Work: -------------------------------------------- 1. Hierarchical Product Classification: Designed a generative LLM-powered system to enable accurate product categorization within complex hierarchies, additionally updated this classification system to handle vague customer requests by dynamically generating multi-hierarchy categorizations. 2. AI-Powered Data Enrichment: Built a scalable pipeline for attribute design, value normalization, and information extraction using multimodal LLMs. 3. Dialog Agents for Dynamic Search Query Construction: Engineered conversational agents that enhance Wizard's search and recommendation systems with adaptive query building. 4. Advanced Customer Review Analysis: Developed LLM-based solutions for review summarization, sentiment mining, and aspect detection. 5. Synthetic Data Generation: Implemented an LLM-driven synthetic data generation service to improve internal model training and evaluation. 6. User Simulation for System Evaluation: Created simulators to streamline training data creation and system testing. 7. Recommendation Diversity Optimization: Designed optimization systems to enhance diversity and relevance in recommendations. Academic Foundations & Past Experience : -------------------------------------------- Completed my Ph.D. in Computer Science at Tulane University, where my research, under the guidance of Dr. Aron Culotta, focussed on understanding user engagement with onl
Tulane University
Doctor of Philosophy - PhD, Computer Science
January 1, 2018 – January 1, 2023
Illinois Institute of Technology
Master’s Degree, Computer Science
January 1, 2015 – January 1, 2017
BMS Institute of Technology and Management
Bachelor’s Degree, Mechanical Engineering
January 1, 2009 – January 1, 2013
Wizard AI
Senior Lead Machine Learning Engineer
October 1, 2024 – Present
Remote
Wizard AI
Lead Machine Learning Engineer
September 1, 2023 – October 1, 2024
Remote
Tulane University
Research Assistant
September 1, 2020 – May 1, 2023
New Orleans, Louisiana, United States
Pacific Northwest National Laboratory - PNNL
PhD Intern - Data Sciences and Analytics Group
June 1, 2020 – August 1, 2020
Remote
Illinois Institute of Technology
Graduate Teaching Assistant
August 1, 2019 – December 1, 2019
Pacific Northwest National Laboratory - PNNL
PhD Intern - Data Sciences and Analytics Group
June 1, 2019 – August 1, 2019
Richland/Kennewick/Pasco, Washington Area
Illinois Institute of Technology
Research Assistant - Tapi Lab (Text Analytics for Public Interest)
September 1, 2018 – June 1, 2020
Accenture
Artificial Intelligence Engineer
August 1, 2017 – May 1, 2018
Chicago, Illinois, United States
Datacubes Inc.
Data Scientist Intern
June 1, 2017 – August 1, 2017
Schaumburg, Illinois
Accenture
Software Engineering Analyst
December 1, 2013 – July 1, 2015
Bengaluru Area, India
Community Detection and Clustering of Twitter Social Networks
September 1, 2016 – Present
Implemented a clustering system that collected the followers of Ellon Musk and the followers of followers of Elon Musk to cluster and identify communities present in this one Hop Network.
Database Backend Design and Development
September 1, 2016 – Present
Designed and developed a database in C with all the ACID properties exhibited by a traditional relational Database. The database had a storage manager, buffer manager using concurrent transaction using the techniques of FIFO, LRU, LFU and CLOCK page replacement policies. Also implemented a Record Manager that was responsible for inserting, updating and deleting large number of records. Also Implemented Indexing Strategies using B+ trees and Hashing Techniques.
Sentiment Analysis and Classification on Tweets relating to Donald Trump and the Republican Party
September 1, 2016 – Present
This was sentiment analysis system constructed to analyze the sentiment towards the tweets written by people about Donald Trump and the Republican Party. This was implemented in Python using a Support Vector Machine for the Classification.
Search Engine for Wikipedia:
March 1, 2016 – Present
Created a search engine using concepts of information retrieval for the Wikipedia database
Comparison of Extreme Gradient Boosting and Logistic Regression for different data sets:
February 1, 2016 – Present
Implemented XGBoost for classification from scratch using Decision Trees and KL- divergence as loss function and compared the performance with Regularized Logistic Regression for real world data sets like Handwritten Letter Recognition, Skin Non-Skin dataset, Bank note Authentication and Iris. Technologies used: Python, numpy.
Online Advertising Directory
September 1, 2015 – Present
Created an online advertising directory in JAVA using RESTFUL web services using Object Oriented design Principles.
Scratch to Scale: Large-Scale Training in the Modern World
Maven
June 24, 2026 – Present
Doctor of Philosophy - Computer Science
Tulane University
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio shows a strong inclination towards research-oriented and academic challenges, which aligns well with roles requiring deep technical investigation and innovation. The progression through academic and industry roles, including lead positions, suggests ambition and a drive for growth. However, the project descriptions lack explicit mention of collaborative efforts or team-based achievements, which could be a point for further exploration regarding cultural fit in a highly collaborative environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and independent approach to problem-solving, often tackling complex technical challenges. The academic and research background suggests a strong capacity for analytical thinking and continuous learning. However, without specific behavioral assessment data, it's difficult to fully assess operational fit and soft skills like teamwork or leadership beyond what can be inferred from lead roles.