Contact

shivanshikaranjai@gmail.com

9883466763

Kolkata

Full Stack Development

Skills

Core Engineering: Software Engineering Fundamentals AI & Machine Learning Concepts. Methodologies: Corporate Software Simulation Workflows Agile Frameworks (Deloitte/Siemens Context). Soft Skills: Analytical Problem Solving Technical Communication Collaborative Team Dynamics.

Links

LinkedIn: www.linkedin.com/in/shivanshi-karanjai-56b425330

GitHub: github.com/soulfulshivu

Summary

Driven and detail-oriented Computer Science & Engineering (AI & ML) undergraduate with a strong foundation in software engineering principles, artificial intelligence, and machine learning. Proven track record of navigating complex industry workflows through highly selective corporate job simulations with global firms like Siemens, Deloitte, and Tata. Certified by Microsoft and an active participant in advanced AI workshops, aiming to leverage technical expertise in an impactful Software or AI Engineering role.

Experience

Project Manager Job Simulation — June 2026

Siemens

Data Analysis Job Simulation — June 2026

Deloitte

Data Visualization Job Simulation — June 2026

TATA

Front End Software Engineering Job Simulation — June 2026

Skyscanner

Education

10+2 — Passing Year :2024

SOUTH POINT HIGH SCHOOL

Graduation — 2024-2028

GHANI KHAN CHOUDHURY INSTITUTE OF ENGINEERING AND TECHNOLOGY

Projects

Smart Load Forecasting & Data Analytics System using AI
• Core Objective: Leveraged machine learning models to forecast electrical energy demand, enabling better grid management and reducing power wastage.
• Technologies Used: Python, Pandas, Scikit-Learn, Matplotlib, and Prompt Engineering (for rapid code generation and testing).
• Implementation:
o Developed an AI-driven predictive regressor model to forecast daily electrical load demand based on historical power consumption and environmental data.
o Utilized advanced AI data analysis tools to preprocess raw sensor datasets, handle anomalies, and extract key features efficiently.
o Implemented and evaluated machine learning algorithms (Decision Trees/Regression Models) utilizing Scikit-Learn to map peak energy demands accurately.
o Leveraged AI prompt engineering to rapidly prototype, test, and debug the Python script framework, optimizing development cycles.