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Vijay RV

@vijayrv

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AI/ML-focused software engineer building scalable, practical ML products.

India

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What I'm looking for

I seek an engineering role building ML-driven products where I can ship models to production, collaborate cross-functionally, and grow in model deployment, reliability, and product impact.

I am an AI/ML-focused software engineer with hands-on experience building production-ready systems and research prototypes. I blend applied machine learning, backend development, and web engineering to deliver scalable solutions that drive measurable impact.

In industry, I led development of an automated web-scraping pipeline and a responsive eligibility portal, reducing manual effort and improving performance and mobile compatibility. In research, I implemented a multimodal emotion-detection model that achieved 92% accuracy and contributed to manuscripts on explainability.

My projects include an AI resume-screening platform using LLMs and FastAPI, a multimodal emotion detection API, and an NLP symptom-checker chatbot, each designed for reliability, scalability, and real-world utility. I prioritize data integrity, robust evaluation, and cross-functional collaboration.

I hold practical experience with Hugging Face models, TensorFlow/Keras, Flask/FastAPI, React and MongoDB, and I seek roles where I can architect ML-driven products, mentor peers, and help move models into production while maintaining strong engineering practices.

Experience

Work history, roles, and key accomplishments

Software Engineer Intern

GetCopayHelp

Dec 2024 - Feb 2025 (2 months)

Spearheaded a Playwright-based Python web-scraping pipeline that automated 90% of drug-metadata ingestion and reduced manual effort by 60%; implemented a responsive Tailwind CSS/JavaScript eligibility portal that cut page load time by 30% and ensured 100% mobile compatibility.

Python Playwright Web Scraping Tailwind JavaScript Data Integrity Performance Optimization

Research Intern

Chennai Institute of Technology

Nov 2024 - Jan 2025 (2 months)

Implemented a multimodal emotion-detection model fusing XLM-RoBERTa and ResNet-50 achieving 92% accuracy on a 15K multilingual dataset (manuscript published) and evaluated XAI techniques (SHAP, LIME, Attention) to inform a novel explainability framework (manuscript under review).