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AI Leader
Himanshu Shivhare is a seasoned professional with extensive expertise in AI and related fields. With a solid background spanning AI, GenAI, machine learning, NLP, computer vision, and more, Himanshu has consistently delivered successful outcomes across various industries including Banking, Insurance, Finance, Retail, and Education. During his time at EY India, Himanshu led key client engagements in AI, Generative AI, ML/DL, NLP, and CV, achieving high levels of customer satisfaction in Banking, Insurance, and Retail. His previous roles at IBM and Cognizant Technology Solutions further solidified his proficiency in delivering tailored solutions to diverse client needs. With a master's degree in computer science specializing in AI from The Ohio State University, Himanshu brings both academic knowledge and practical experience to the table. His focus on solution design, technological innovation, and team development ensures not just project success but also long-term organizational growth.
The Ohio State University
Master's degree, Computer Science
January 1, 2011 – January 1, 2014
GCET
B. Tech., Computer Science
January 1, 2004 – January 1, 2008
Tredence Inc.
Director AI and Data Science
January 1, 2026 – Present
IBM
Senior Manager AI
October 1, 2023 – January 1, 2026
Bengaluru, Karnataka, India · On-site
EY
Data Science Manager
October 1, 2021 – October 1, 2023
Bengaluru, Karnataka, India
IBM India Private Limited
Data Science Manager
March 1, 2017 – October 1, 2021
Bengaluru, Karnataka, India
Crimson Interactive Pvt. Ltd.
Lead Data Scientist
June 1, 2016 – March 1, 2017
Mumbai, Maharashtra, India
ClearEdge3D, Inc.
Lead Software Engineer (ML)
January 1, 2016 – May 1, 2016
Manassas, VA
Cognizant Technology Solutions
Data Engineer
June 1, 2014 – December 1, 2015
Dublin, OH, Columbus, OH, San Jose, CA
The Ohio State University
Graduate Student - Computer Science
May 1, 2012 – May 1, 2014
NEC
Technical Lead - Language Processing Domain
January 1, 2009 – August 1, 2011
Noida, India
MBIT Wireless
Development Engineer
July 1, 2008 – December 1, 2008
Greater Chennai Area
Automated Legal Research
January 1, 2018 – January 1, 2019
System designed to use state of the art summarization and NLU techniques to automate legal research. It uses Encoder decoder based Pointer generator networks along with LSTM networks to accomplish the task of text comparison in legal domain.
Customer transaction pattern profiling
October 1, 2015 – October 1, 2016
• The goal of this project was to develop a system that used transactional data from Kohl’s online and profile the transactional patterns of customers • The system used a Hadoop/Map reduce based backend to preprocess the massive amount of data • Then we used a K-means based clustering scheme to profile the users and use the insights for marketing and user experience • Tools/Resources: Java, SQL server, Sqoop, Hadoop, Map-reduce
BCBS data migration
June 1, 2014 – October 1, 2014
• The goal of this project was to develop a system that analyzed the data migration being undertaken as part of the JP Morgan Chase BCBS initiative • The system imported massive amounts meta data from a DB2 based data warehouse into a Hadoop based processing framework • The system performed health and sanity checks based on business logic and then produces reports on progress and performance of the migration along with any issues • Tools/Resources: SQL, Java, Hadoop, Map-reduce
Semantic Search
May 1, 2014 – Present
Goal: To develop an intelligent semantic search engine for users Using Neural Network and clustering techniques to train the model for user preferences Search thousands of resumes based on user query and past preferences Tools/Resources: C#
Analytical job board with semantic search
May 1, 2013 – Present
• The goal of this project was to develop a distributed job board for Zembretta Group with an analytical backend and semantic search engine • Design a HDFS based storage scheme to store and process the large amount of data using map reduce • Developed predefined analytical functionalities in the system to provide processed inferences and trends • Using KNN algorithm and clustering techniques to train the model for user preferences, profile and past choices • Tools/Resources: Java, Hadoop
Network Base Intrusion Detection System using SVMs
August 1, 2012 – December 1, 2012
Developed a network based intrusion detection system using the KDD ’99 data System specifically targeted to detect Denial of Service attacks System uses SVM learners to model the attack pattern based on multiple features
Cultural Fit Analysis
The candidate's career progression through various companies (IBM, EY, Tredence) and diverse project portfolio (job boards, legal research, customer profiling, intrusion detection) suggest adaptability and a broad interest in applying ML across different domains. Their leadership roles in building AI CoEs and mentoring teams indicate a collaborative and growth-oriented mindset, which generally aligns well with a positive cultural fit.
Soft Skills & Operational Fit
The candidate's experience descriptions highlight strong leadership, team management, stakeholder management, and cross-functional collaboration skills. They have experience defining SLAs, KPIs, and driving operational excellence in AI product delivery, indicating a good fit for a senior operational role.