Ranjeet Gupta

I'm AI Engineer

About Me

AI/ML Engineer working on production-grade deep learning, Generative AI systems, and automated MLOps pipelines.

Education

  • University: National Institute of Technology, Jamshedpur (Course: B.Tech)
  • Higher Secondary: Akanksha 40, JAC Board (92%)
  • Secondary Education: Model School (90%)

Core Skills

Python C/C++ SQL Machine Learning Deep Learning Computer Vision Agentic & Gen AI PyTorch TensorFlow LangChain/LangGraph ETL & Analytics AWS & Azure Docker/Kubernetes MLOps & CI/CD FastAPI

Hi

I'm Ranjeet Gupta, a B.Tech student at NIT Jamshedpur with a strong passion for Artificial Intelligence, Machine Learning, and Data Science. I enjoy building intelligent systems that combine LLMs, RAG pipelines, multi-agent architectures, and predictive analytics to solve real-world problems. My experience spans AI research, statistical modeling, computer vision, and autonomous robotic systems. I have worked on projects ranging from multimodal AI assistants and Hybrid RAG systems to foundation models and advanced deep learning architectures. I am driven by curiosity, innovation, and the goal of creating impactful AI solutions that bridge research and practical applications.

Experience

Intern

May – June 2025

Gargs Engineering Limited

  • Performed deep statistical analysis and cost structure evaluations to optimize manufacturing operations, reducing production cost by approx 8%.
  • Developed interactive dashboards Power BI to analyze material flow and revenue trends.
  • Applied design optimization principles and statistical modeling for continuous product improvement.

Intern

Oct 2024 – Jan 2025

IIT Hyderabad Sponsored Research Project

  • Modeled complex vehicle dynamics using data-driven statistical analysis and advanced optimization techniques.
  • Applied optimization methods to enhance locomotion stability and optimize flapping frequency responses.
  • Analyzed and validated experimental data streams to verify predictive system behavior and control accuracy.

Co-Founder

Aug 2024 – Present

Robotic and Automation Club, NIT Jamshedpur

  • Co-founded a research-oriented student club focusing on machine learning and intelligent automation systems.
  • Mentored students on machine learning pipelines, deep learning frameworks, and computer vision architectures.
  • Directed the design and development of end-to-end computer vision and embedded AI proof-of-concepts.

Projects & Patents

Intellectual property, published patents, and academic research projects in Artificial Intelligence and Machine Learning.

  • All
  • Agentic & Gen AI
  • ML & DL
  • Advanced AI & Deep Learning
  • Data Analysis
Vehicle Insurance Pipeline

Vehicle Insurance Pipeline

A robust MLOps pipeline for managing vehicle insurance data featuring automated processing and model deployment.

Tech Stack: Python, MLOps, MongoDB Atlas, AWS, Docker, GitHub Actions, Flask.
BusinessIQ Platform

BusinessIQ

Agentic AI Platform for Analytics, RAG, and WhatsApp Marketing Automation.

Approach: Multi-agent architecture using LLMs for SQL translation, vector databases for RAG, and automated routing for WhatsApp workflows.
Multimodal AI Perception System

AL Humanoid Robotic Head

A personalized humanoid robotic head designed for natural human-machine interaction, built to provide multipurpose, domain-specific customer support.

Approach: Powered by a multimodal AI architecture integrating on-device computer vision and Gemini for real-time perception and tailored conversational support.
Movie Recommender System

Movie Recommender System

A content-based filtering recommender that suggests similar movies using NLP and cosine similarity on the TMDB 5000 dataset.

Approach: Extracted features using NLP techniques (TF-IDF/CountVectorizer) and computed cosine similarity matrices to rank and suggest similar content.
Foundation Models for Event Reconstruction

Foundation Models for Event Reconstruction

A unified, multi-task AI model built for holistic physics event analysis.

Problem & Solution: Standard pipelines process tasks separately, causing compounding errors. We built components for a unified model handling multiple tasks simultaneously, incorporating a custom ConservationLoss to strictly follow the laws of conservation of energy.
Tech Stack: Python, PyTorch, Physics-Informed ML.
Particle Track Reconstruction

End-to-End Particle Track Reconstruction

A graph-based machine learning pipeline designed to map the chaotic paths of particles during high-energy collisions.

Problem & Solution: Overlapping collision paths and background noise make tracking difficult. We implemented Graph Neural Networks (GNNs) to treat individual particle hits as nodes and accurately connect them into full trajectories.
Tech Stack: Python, PyTorch Geometric, GNNs, Data Pipelines.
CMS Detector Super-Resolution

CMS Detector Super-Resolution

A deep learning project that increases the resolution of particle collision data from the CMS detector at CERN.

Problem & Solution: Storing and processing massive amounts of high-resolution sensor data is incredibly slow and expensive. Built a deep learning network that reconstructs lightweight data back into high-fidelity data.
Tech Stack: Python, PyTorch, Deep Learning, Super-Resolution Networks.
Used Car Sales Data Analysis

Used Car Sales Dashboard

An interactive Tableau dashboard analyzing used car sales data to uncover insights on pricing trends, fuel types, and ownership patterns.

It transforms raw automobile listings into clear, data-driven stories, helping stakeholders make informed decisions about inventory and market trends.

Approach: Cleaned raw datasets, built relational models in Tableau, and designed interactive visualizations with dynamic filters.
Mobile Smartphone Sales Dashboard

Mobile Sales Dashboard

A Power BI dashboard visualizing smartphone sales across India to highlight key business insights like total sales, payment trends, and top-performing models.

It provides a comprehensive overview of customer demographics and regional distribution to drive data-informed business strategies.

Approach: Ingested unstructured data into Power BI, performed DAX modeling, and developed interactive dashboards to track KPIs.
  • 1
  • 2

Contact

Feel free to reach out for research collaboration, internship opportunities, or AI engineering roles.

Address

NIT Jamshedpur, Jharkhand, India

Call me

+91 7858823496

Email me

ranjeetgupta.work@gmail.com

RG

Ranjeet's Inbox Assistant

Online • Interactive Contact Chat
Step 1 of 4
AI
Hello! I'm Ranjeet's Inbox Assistant. If you'd like to leave a message, let's start with your name. What should Ranjeet call you?