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Assessing your cultural and operational fit
AI Engineer with less than a year in AI/ML, Full-Stack Development & Data Science
B.Tech final-year student (Electronics & Computer Engineering, 2026) with expertise spanning AI/ML, Generative AI, Full-Stack Development, Data Science, Data Engineering, Cyber Security, DevOps, and Python Development. Experienced building production-style ML pipelines, REST APIs, ETL pipelines, BI dashboards, and IoT systems through internships and deployed projects. Hands-on experience as Data Analytics Trainee at Scatterpie Analytics and Data Analyst Intern at Godrej Infotech Ltd. — built Python data pipelines, AI/ML models (TensorFlow, Scikit-learn, XGBoost), Flask REST APIs, and Power BI dashboards used by real stakeholders. Designed an ML pipeline reducing stock-out risk by 15%, and built 12+ Power BI dashboards backed by optimized SQL data models. Core stack: Python, TensorFlow, PyTorch, Scikit-learn, XGBoost, OpenCV, SQL, MongoDB, Power BI, Flask, Django, React, Node.js, Git, Docker. Also work with OpenAI/Anthropic APIs, LangChain, and prompt engineering for AI-powered features. Independently built and deployed: Customer Churn Prediction System with LLM-based insights and Power BI dashboard; Smart Farm AI/ML Control Center with CNN disease detection and IoT anomaly detection; Crypto Fraud Analysis Platform for fraud and market trend detection. IEEE published researcher; ranked Top 30/500+ at a national Cyber Security Hackathon at D.Y. Patil University. Versatile, full-stack mindset combining data engineering, ML, software development, and BI — actively seeking opportunities across AI/ML, data, and full-stack roles.
Sanjivani Rural Education Society`S Sanjivani College Of Engineering
B.Tech · Electronics and Computer Engineering
November 21, 2022 – June 29, 2026
Analytics Job
Data Analyst Trainee
August 14, 2025 – November 14, 2025
Aurangabad, Maharashtra, India
Smart Farm AI/ML Control Center
March 11, 2026 – May 11, 2026
Built a comprehensive AI/ML-powered smart agriculture platform for crop monitoring, plant disease detection, and yield optimization. Developed a CNN-based plant disease detection model using TensorFlow and OpenCV achieving 80%+ accuracy, a Random Forest crop recommendation engine (90%+ accuracy) based on soil nutrients and weather data, and an XGBoost yield prediction model. Simulated real-time IoT sensor data with Isolation Forest anomaly detection to flag abnormal environmental readings. Deployed all models as Flask REST API endpoints with SQLite database logging, and launched the complete platform as a live web application.
View ProjectCrypto Fraud Analysis Platform
February 6, 2026 – April 6, 2026
Built an ML-powered cryptocurrency fraud detection and market analysis platform applying Scikit-learn classification models (Random Forest, XGBoost) to identify fraudulent transactions and anomalous trading patterns. Implemented time-series market trend prediction using Pandas, built a Flask REST API backend, stored transaction data in SQL, and deployed the platform as a live web application with Git version control.
View ProjectData Science & Artificial Intelligence Training
Sanjivani Artificial Intelligence Technologies, Sanjivani COE Kopargaon
July 10, 2024 – Present
Achieved a perfect score, indicating comprehensive knowledge and practical expertise in Power BI.
Strengths
Limitations
Cultural Fit Analysis
The candidate's diverse projects (smart agriculture, crypto fraud, customer churn prediction) demonstrate a broad interest in applying AI/ML to various domains. The involvement in a Cyber Security Hackathon and an IEEE publication indicates a proactive and learning-oriented mindset. The target role of AI Engineer aligns well with the candidate's project work and technical skills, suggesting a good cultural fit for an innovation-driven environment. However, the psychometric test score is a concern.
Soft Skills & Operational Fit
The candidate's project descriptions and internship experience suggest an ability to work in Agile environments, collaborate on codebases (Git), and deliver end-to-end solutions. The psychometric test score is below average, which might indicate potential areas for development in logical reasoning, stress handling, or team collaboration, requiring further validation during interviews.
Scored 95%, demonstrating a very strong grasp of full-stack Python development, with only minor room for improvement.
Strengths
Limitations