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vaidik pipaliya

@vaidikpipaliya

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AI/ML Engineer building production LLM (RAG) and computer-vision systems with full-stack delivery.

Zimbabwe

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What I'm looking for

I’m looking for a role where I can build production LLM/RAG and computer-vision systems, deliver full-stack features in FastAPI, and focus on reliability (verifiable citations) and explainability (e.g., Grad-CAM).

I’m an AI/ML Engineer focused on building production-grade AI systems using LLMs and RAG, alongside computer vision and full-stack development. I bring hands-on experience turning model ideas into real applications that users can rely on.

In my projects, I led the development of citation-backed RAG document analysis with FastAPI, LangChain, and ChromaDB, processing 10,000+ word documents with 100% verifiable citations. I work to eliminate hallucinations using custom post-generation text matching and improve multilingual retrieval with dynamic language detection.

I also build explainable computer-vision solutions. For AgriVerse, I integrated Grad-CAM to generate spatial heatmaps for model transparency, and I created a FastAPI backend for real-time image inference and weather-based agricultural insights.

Alongside engineering, I’ve advanced research on efficient plant disease detection using Transfer Learning (MobileNetV2) and ExplainableAI (Grad-CAM). During my internship at we3vision, I built multiple full-stack web applications with live data handling and modular Python backend architecture, and I earned a winner award at the E.D.G.E. 2025 AI/ML track for an innovative mini-project.

Experience

Work history, roles, and key accomplishments

Software Developer Internship

We3vision

Dec 2024 - Apr 2025 (4 months)

Built 4+ full-stack web applications using Python, JavaScript, and Flask with live data handling. Architected modular Python backends using MVC patterns and delivered responsive frontends with HTML/CSS/JavaScript.

Python JavaScript Flask HTML CSS MVC Databases

Plant Disease Detection Framework

Independent Research (Unpublished)

Developed an interpretable plant disease detection framework using MobileNetV2 transfer learning to classify diseases under complex visual conditions. Implemented Grad-CAM explainability and spatial data augmentation, and proposed an extension to multimodal AI integrating LLM and RAG.

MobileNetV2 Transfer Learning Explainable AI Grad CAM Data Augmentation (Rotation Zoom Multimodal AI LLM RAG

Education

Degrees, certifications, and relevant coursework

MIT-WPU University

Master of Computer Applications (MCA), Machine Learning & Deep Learning

2025 -

Currently pursuing a Master of Computer Applications (MCA) focusing on machine learning, deep learning, data structures & algorithms, and database management.

P. P. Savani University

Bachelor of Computer Applications (BCA), Software Engineering & Data Analysis

2022 - 2025

Grade: 7.58

Completed a Bachelor of Computer Applications (BCA) with coursework in Python programming, web technologies, data analysis, and software engineering, graduating with a CGPA of 7.58.