P. TANURITHA
Career Objective
Frontend Developer with 0+ years of experience designing component-driven React UIs and shipping full-stack projects through CI/CD pipelines. Skilled in React.js, HTML5 and CSS3 for building responsive, accessible interfaces and in Java with Spring Boot for consuming and integrating REST APIs. Experienced deploying containerized applications to AWS EC2 using Docker and automating releases with Jenkins pipelines tied to GitHub. Developed normalized MySQL schemas and data access layers to support multi-user transactional workloads. Applied Python-based machine learning workflows using TensorFlow, OpenCV, NLP techniques, Whisper and Transformer models for healthcare and career-recommendation prototypes. Comfortable owning frontend architecture end-to-end, embedding analytics into UIs, and producing reproducible CI/CD artifacts for production-like environments. Seeking an entry-level Frontend Developer role to leverage React and cloud deployment experience to deliver polished, production-ready user experiences.
Education
Technical Skills
Projects
- Architected and implemented a component-driven React frontend and state model for the expense dashboard using React.js, HTML5 and CSS3.
- Implemented secure session-based authentication and REST API integration to consume Spring Boot endpoints for expense CRUD operations.
- Designed a normalized MySQL schema and implemented server-side data access layers to support multi-user expense records and category-wise reporting.
- Containerized frontend and backend components with Docker to ensure environment parity across development and deployment.
- Built a Jenkins CI/CD pipeline to automate build and test stages and trigger deployments from the repository.
- Developed an interactive Streamlit UI to collect student skill profiles and present personalized career-path recommendations.
- Engineered data ingestion and feature extraction pipelines in Python to normalize user inputs for model training.
- Trained and evaluated machine learning recommendation models using NLP-derived features to identify skill gaps and suggest learning paths.
- Integrated model outputs with the frontend to provide on-demand skill-gap reports and visualized progression pathways.
- Trained convolutional neural networks in TensorFlow for medical image classification, achieving 87.8% accuracy on the test set.
- Implemented image preprocessing and augmentation pipelines using OpenCV to improve model robustness and reduce overfitting.
- Optimized training workflows and dataset pipelines to accelerate convergence and improve generalization.
- Deployed the trained model as a Flask web service exposing a prediction endpoint for real-time inference.
- Built an audio preprocessing pipeline and integrated OpenAI Whisper for reliable speech-to-text transcription.
- Applied Transformer-based summarization models to generate concise abstractive summaries from transcribed text.
- Packaged the transcription and summarization components into a Flask API and lightweight UI for end-to-end demonstrations.
Certifications
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