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AI Engineer with 2+ years in Machine Learning & NLP
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Machine Learning and Data Science professional with expertise in Python, SQL, Machine Learning, Deep Learning, NLP, and Time-Series Forecasting. Skilled in developing end-to-end ML solutions, including data preprocessing, feature engineering, and model optimization. Proficient in API development using FastAPI, creating interactive dashboards with Streamlit, and deploying solutions in the cloud using Render and Streamlit Cloud. Passionate about leveraging AI-driven solutions for business problem-solving and data-driven decision making.
Lovely Professional University
Master of Business Administration · Data Science & Business Analytics
August 1, 2025 – Present
Mangalayatan University
Master of Computer Applications · Machine Learning & Artificial Intelligence
August 1, 2022 – June 30, 2024
Labmentix
Data Science Trainee | Internship
April 1, 2026 – Present
Bengaluru, Karnataka, India
Customer Segmentation & Behavioral Analytics Platform
June 1, 2026 – Present
Developed an end-to-end customer segmentation platform leveraging K-Means Clustering and PCA. Executed data cleaning, feature engineering, scaling, and dimensionality reduction on customer behavioral data. Identified the optimal number of customer segments using the Elbow Method and Silhouette Score. Created an interactive dashboard with Streamlit and deployed a production-ready API utilizing FastAPI, Docker, Render, and Streamlit Cloud. Facilitated business insights for customer retention, targeted marketing campaigns, and personalized recommendations.
View ProjectLoan-Approval-Prediction-System
June 1, 2026 – Present
Developed an end-to-end machine learning solution to predict loan approval decisions based on applicant demographics, financial history, credit score, debt-to-income ratio, and collateral information. Executed data preprocessing techniques including missing value imputation, exploratory data analysis (EDA), feature encoding, and feature scaling. Engineered new features through polynomial transformations and logarithmic scaling to enhance model performance. Trained and evaluated Logistic Regression, K-Nearest Neighbors, and Naive Bayes classifiers using evaluation metrics such as Accuracy, Precision, Recall, and F1-Score. Achieved 88% classification accuracy and an F1-score of 0.81 with Logistic Regression following feature engineering. Identified Credit Score and Debt-to-Income Ratio as the most influential factors affecting loan approval outcomes.
View ProjectNLP-Based Sentiment Analysis Framework
June 1, 2026 – Present
Developed an end-to-end sentiment analysis framework to classify textual data as positive or negative. Created NLP pipelines for text cleaning, tokenization, stop-word removal, stemming, and feature extraction. Implemented and compared various text vectorization techniques, including Bag of Words (BoW), TF-IDF, and Word2Vec. Trained and evaluated machine learning models for sentiment classification using Movie Review and Twitter datasets. Conducted performance analysis using Accuracy, Precision, Recall, and F1-Score metrics. Designed reusable preprocessing and prediction pipelines for real-time sentiment scoring.
View ProjectData Science & Machine Learning Bootcamp
Unknown
June 1, 2026 – Present
Python Programming Bootcamp
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, covering customer segmentation, loan prediction, and sentiment analysis, indicates a broad interest in applying AI/ML across different domains. The academic background in Data Science & Business Analytics and Machine Learning & Artificial Intelligence, combined with an internship, shows a strong commitment to the field. The active participation in Kaggle and self-learning in MLOps and Cloud AI suggests a proactive and continuous learning mindset, which is a good cultural fit for a dynamic AI engineering role.
Soft Skills & Operational Fit
The candidate demonstrates soft skills such as teamwork, leadership, adaptability, problem-solving, and decision-making through project descriptions and listed skills. The operational fit is good given the focus on end-to-end ML solutions and deployment, which aligns with typical AI Engineer responsibilities.