Niraj Gupta
ML EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Experienced Backend Developer | Building Scalable Solutions | IIT Dhanbad
Backend Engineer with 3+ years of experience building scalable systems across logistics tech domain. Currently an SDE-2, I design robust backend architectures using Ruby on Rails, Golang, PostgreSQL, and distributed systems, driving measurable impact like 25% higher engagement, 30% customer growth and and 30% operations growth through automation and better-integrated systems. Experienced in event-driven systems, automation engines, and performance optimization with Redis and Sidekiq. I bring a strong product mindset—focusing on solving real business problems, not just building features. Also comfortable working across the stack, including React and ML-powered solutions.
Indian Institute of Technology (Indian School of Mines), Dhanbad
Master of Technology - MTech, Computer Science
January 1, 2020 – January 1, 2022
Savitribai Phule Pune University
Bachelor of Engineering - BE, Computer Engineering
January 1, 2015 – January 1, 2019
Birla School, Kalyan
Higher Secondary, Computer Programming
January 1, 2013 – January 1, 2015
VEGROW
SDE 2 - Full Stack
August 1, 2025 – Present
Hybrid
VEGROW
Software Developer
January 1, 2024 – August 1, 2025
Hybrid
Cogoport
Associate Software Engineer
June 1, 2022 – October 1, 2023
Mumbai, Maharashtra, India · On-site
Skill Safari
Machine Learning Intern
May 1, 2021 – July 1, 2021
Restaurant Recommendation Systems
April 1, 2020 – May 1, 2020
Restaurant recommender system is a machine learning model, developed to demonstrate as a capstone project to IBM through coursera. It recommends restaurants based on the user's likes and dislikes and his previous interest data. The result of the recommender system is that it produces a list of top restaurants and the most common venue item that the user can enjoy.
IBM Data Science Specialization
Coursera
June 23, 2026 – Present
Cultural Fit Analysis
The candidate has experience in diverse roles, from ML Intern to SDE 2 Full Stack, across different companies (VEGROW, Cogoport, Skill Safari). The project 'Restaurant Recommendation Systems' and the 'Visual Intelligence module' at VEGROW show initiative and practical application of ML. The target role is ML Engineer, and while the candidate has relevant ML experience, a significant portion of their professional experience is in full-stack development. This suggests a potential fit for roles requiring both ML and strong engineering skills, but a pure ML-focused role might require further validation of deep ML expertise.
Soft Skills & Operational Fit
The candidate's experience descriptions suggest problem-solving abilities (resolving critical support issues, bug fixing) and collaboration (providing consistent support to team members). The project descriptions indicate an ability to deliver features and systems from design to implementation. However, specific soft skill assessments are not available from the provided data.