NLP Engineer with 0.5 years of practical experience building end-to-end text classification pipelines and deploying interactive ML apps. Strong Python background with hands-on use of pandas and NumPy for ETL, TF-IDF feature engineering and scikit-learn for classical ML (Logistic Regression). Experienced in deploying models with Streamlit and producing reproducible model artifacts using joblib. Delivered an NLP solution to detect fraudulent job postings and processed 20,000+ records; also adapted MobileNetV2 in TensorFlow for medical image classification with dataset expansion to 3,264 images. Comfortable working with REST APIs, JSON, and SQL for data ingestion and storage. Seeking an entry-level Software Developer or NLP-focused role where I can contribute to model development, deployment, and data-driven feature engineering.
Technical Skills
Programming Language: Python,SQL,Regex
Frontend Technologies: HTML,CSS
API & Integrations: REST APIs,JSON
Data Analysis & Visualization: Streamlit,Pandas,NumPy,Data Augmentation,Learning Rate Scheduling,requests
Processed and analyzed 20,000+ job-posting records using pandas and NumPy with regex-based cleaning to prepare textual data for modeling.
Engineered TF-IDF feature pipelines and applied dimensionality reduction to produce stable feature inputs for classifiers.
Trained and validated Logistic Regression classifiers using scikit-learn with cross-validation and class-weighting, achieving 85% classification accuracy on a held-out test set.
Optimized model robustness by tuning hyperparameters and adding n-gram features, improving F1 performance versus baseline.
Deployed an interactive Streamlit web app to enable reviewers to triage flagged job postings and automate parts of the review workflow.
Stored processed datasets and model outputs in SQL tables for reproducible analysis and downstream reporting.
Documented model evaluation, designed modular preprocessing and model classes following OOP principles, and persisted production models using joblib for handoff.
Projects
Real-Time Weather Application Using Python and REST API
Built a Python client to fetch real-time weather data from the OpenWeatherMap REST API using requests and parsed JSON responses.
Designed a simple HTML/CSS front-end to present temperature, humidity, wind speed and weather conditions retrieved from the API.
Modified MobileNetV2 for Parkinson’s Disease Detection
Tools Used: TensorFlow, MobileNetV2, Data Augmentation, Dropout, Learning Rate Scheduling
Adapted the MobileNetV2 architecture in TensorFlow for medical image classification, retraining the final layers on a Parkinson’s disease imaging dataset.
Expanded the dataset from 204 to 3,264 images using data augmentation and improved generalization by adding dropout and learning-rate scheduling, boosting inference performance.
Education
Vignan University
Master of Computer Applications (MCA) • Oct 2024 – Jul 2026
Aditya Degree College
Bachelor of Computer Applications (BCA) • Nov 2021 – Apr 2024
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