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AI Solutions | Royal & Family Office Consulting| Precious Metals Advisory | Oil and Petroleum products
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- Delivering AI and LLM-based solutions for global enterprises -Enabling trust-driven workflows in physical asset supply chains and commodities trade across Africa, UAE, India, and Asia 🔧 Core strengths: AI product strategy, GenAI (RAG, Azure OpenAI), and cross-functional execution Open to collaborating on AI/LLM initiatives and building resilient commodity supply chain solutions globally.
Indian maritime university
Bachelor of Technology - BTech, Marine Engineering
January 1, 2013 – January 1, 2017
St. Xavier's School, Jaipur
Class XII, Science
January 1, 2012 – January 1, 2013
St. Xavier's sr. sec. school - Jaipur
10th
January 1, 2010 – January 1, 2011
EPAM Systems
Lead AI Scientist
September 1, 2025 – Present
India · Remote
Product Space
PM fellow
November 1, 2024 – October 1, 2025
Greater Bengaluru Area · Remote
EY
Senior Consultant
May 1, 2024 – September 1, 2025
Gurgaon Rural, Haryana, India · Hybrid
Celebal Technologies
Associate Consultant -Data science
May 1, 2023 – April 1, 2024
Jaipur, Rajasthan, India · On-site
Stride.ai Inc
NLP Engineer
December 1, 2022 – April 1, 2023
Bengaluru, Karnataka, India · On-site
HCL Technologies
Speciallist
October 1, 2021 – November 1, 2022
Delhi, India
Sutherland
Storage Engineer
March 1, 2020 – August 1, 2020
Bengaluru, Karnataka, India
Progressive Infovision Private Limited
Desktop Support Engineer( level 2)
September 1, 2019 – January 1, 2020
Bangalore
Fleet Management Limited
Marine Engineer
January 1, 2018 – March 1, 2019
Travelled globally (Australia, Japan, China, etc.)
Spend Analytics
February 1, 2021 – April 1, 2021
Spend Analytics - Procurement is a very important part of any insutry and thus plays a very crucial role when it comes to optimization of the resources and cost redcution. Obejctives - 1. To determine the purchasing trends from the given unstructerd data of the company. 2. To make the clusters of the materials having similar purchasing trends. 3. To come up with the conclusions which would be effective in taking business decision for cost reduction and optinmization. Pathway of the Project - 1. Ananlysing and Understanding the data to figure the useful features for the further analysis. 2. Data preprocessing and Data cleaning to make neccesasry useful changes in the data. 3. Different visulaisations being made with help of python and BI tool(Tableau). 4. After analysis of the visuslisations based on features(Gross Price, PO quantity) for each material group(having different materials) across all the purchasing timeline, came up with trends for each Material group. 5. Based upon the trends found out , clusters were made of different materials in a particular Material group. This process was carried out for all the groups. 6. For clustering purposes diffrent unsupervising ML algorithms were used and the best result was given by K-Means Clustering. 7. After the clustering process, some final observations along with the conslusion(suggestions for shipping cost reduction) were given.
KPMG- Data Science Pro Degree
Imarticus Learning
June 25, 2026 – Present
data science bootcamp 2020
Udemy
June 25, 2026 – Present
CutShort Certified R Programming - Basic
Cutshort
June 25, 2026 – Present
Cultural Fit Analysis
The candidate's career trajectory shows a significant shift from Marine Engineering to IT support, then to data science and AI. This demonstrates adaptability and a strong drive for career change. The project 'Spend Analytics' shows initiative in applying ML concepts. However, the diversity of projects and roles, while broad, also indicates a less focused path, which might require assessment for alignment with a specific ML Engineer culture. The target role of ML Engineer aligns with recent experience, but the early career is disparate.
Soft Skills & Operational Fit
The candidate's experience as a Senior Consultant at EY and PM Fellow at Product Space suggests exposure to project management, teamwork, and product development, which are valuable for operational fit. However, specific soft skill demonstrations are not detailed in the provided data.