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AI Engineer with less than a year in Python, Machine Learning, and Generative AI.
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Assessing your cultural and operational fit
Results-driven AI/ML Engineer and Data Analyst with hands-on expertise in Python, machine learning, deep learning, NLP, LangChain, LangGraph, and data analysis. Experienced in designing end-to-end AI/ML pipelines, performing exploratory data analysis, building GenAI and Agentic AI solutions, and deploying scalable REST APIs using FastAPI and Docker. Seeking to contribute as an AI/ML Engineer, Data Analyst, or Gen Al Engineer in an innovative, growth-focused organization.
BNN College, Bhiwandi
MSc(I.T)
August 1, 2026 – Present
BNN College, Bhiwandi
Bsc (IT)
August 1, 2024 – Present
BNN College, Bhiwandi
H.S.C
June 1, 2021 – May 31, 2021
Kakatiya High School, Bhiwandi
S.S.C
June 1, 2019 – May 31, 2019
Flight Ticket Price Prediction
June 27, 2026 – Present
Regression-based ML project predicting airline ticket prices from flight features such as airline, route, stops, and duration, enabling data-driven pricing insights. Engineered features from raw flight data including date parsing, duration extraction, and route encoding. Trained and tuned Random Forest and Gradient Boosting regressors; evaluated with MAE and R2. Visualized price distribution and key price-driving features using Matplotlib and Seaborn.
View ProjectInsurance Cost Prediction Using ML & Streamlit
June 27, 2026 – Present
End-to-end ML application predicting insurance premium costs based on policyholder attributes, deployed as a Streamlit app and REST API using FastAPI with Docker containerization. Performed EDA and preprocessing on policyholder data (age, BMI, smoking status, region). Trained regression models (Linear Regression, Random Forest, XGBoost); selected best via RMSE and R2. Deployed trained model as REST API using FastAPI; containerized full application with Docker.
View ProjectCredit Card Fraud Detection Using ML
June 27, 2026 – Present
Binary classification project to detect fraudulent credit card transactions from highly imbalanced financial data using ensemble models and anomaly detection techniques. Performed EDA on imbalanced dataset; applied SMOTE oversampling to balance fraud vs. non-fraud classes. Trained Logistic Regression, Random Forest, and XGBoost; optimized precision-recall trade-off for fraud recall. Visualized confusion matrix, ROC-AUC curves, and feature importance for model interpretation.
View ProjectCertification in Data Science & Data Analytics Using AI
Quality Software Technologies
June 1, 2026 – Present
Certification in Data Pre-processing and Business Analysis
Anudip Foundation
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects are all personal, demonstrating initiative and self-driven learning. The diversity in project types (fraud detection, insurance prediction, flight price prediction) shows a broad interest in applying ML to different domains. The stated career objective aligns well with an AI Engineer role, indicating a clear career path. However, the lack of professional experience or team-based projects limits the assessment of collaboration and adaptability in a corporate culture.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work independently on end-to-end solutions. The focus on practical applications and deployment suggests a results-oriented approach. However, without specific assessment data on soft skills or team collaboration, a comprehensive evaluation of operational fit is limited.