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Dhrumil Prajapati

@dhrumilprajapati

AI/ML Engineer at Clicode building production-grade GenAI applications, agentic workflows, and document intelligence systems.

India

Message

What I'm looking for

I'm targeting AI/ML Engineer roles at an organization of high repute, with scope to improve my knowledge and further my career growth.

At Clicode, I build production-grade Generative AI applications using LLMs, LangChain, LangGraph, RAG, and vector databases. My work includes AI assistants, agentic workflows, and document intelligence systems.

I also develop scalable AI applications with FastAPI, Docker, databases, and cloud-based deployments. I integrate multiple LLM providers and optimize retrieval pipelines for accuracy, latency, and cost.

At Tuvoc Technologies, I developed a complete RAG pipeline using Whisper for transcription, preprocessing, embedding generation, and vector database indexing. I benchmarked embedding models and tuned vector database parameters to enhance search precision.

During my research internship at Space Application Centre, ISRO, I developed a U-Net and ResNet-34 pipeline for land-cover change detection using OSCD and Sentinel-2 imagery. I also built a medical diagnosis RAG system with LangChain, Pinecone, and FastAPI, and a deep-learning model for rooftop solar panel detection.

Experience

Work history, roles, and key accomplishments

Current

AI/ML Engineer

Current

Clicode

Jan 2026 - Present (9 months)

Building production-grade Generative AI applications using LLMs, LangChain, LangGraph, RAG, Vector Databases, and APIs. Developing AI assistants, agentic workflows, document intelligence systems, and enterprise AI solutions.

LLM Lang Chain LangGraph RAG Vector Databases fastAPI Docker cloud deployment API Integration

AI/ML Developer

Tuvoc Technologies

Jul 2025 - Dec 2025 (5 months)

Developed a complete Retrieval-Augmented Generation (RAG) pipeline using Whisper for transcription, text preprocessing, embedding generation, and vector database indexing. Enhanced search precision by benchmarking multiple embedding models and tuning vector DB parameters.

RAG Whisper Embedding models Vector Databases Search Optimization Python

Research Internship

Space Application Centre, ISRO

Jan 2025 - May 2025 (4 months)

Developed a deep learning pipeline utilizing a U-Net architecture with a ResNet-34 backbone for semantic segmentation tasks aimed at detecting land cover changes. Implemented inference procedures to generate change masks for monitoring environmental and urban transformations.

Deep Learning U Net ResNet34 Semantic Segmentation Satellite Imagery GeoTIFF Python