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Md. Ehsanul Haque K. Cloud, Frontend and AI Platforms

My name is Md. Ehsanul Haque K. and I have over 14 years of experience in the tech industry. I specialize in the following technologies: React, Django, HTML5, On-Page SEO, Technical Writing, etc.. I hold a degree in Bachelor of Science (BS). Some of the notable projects I’ve worked on include: RescueVision: AI-Powered Search & Rescue Command Center, AI-Powered Customer Feedback Platform, Prescriptive Maintenance System (ML, LLM, RAG, NLP), AuraScanAI: AI-Powered Vehicle Damage Assessment System, AI Research Assistant (Local LLM & RAG Agent), etc.. I am based in Dhaka, Bangladesh. I've successfully completed 45 projects while developing at Softaims.

I value a collaborative environment where shared knowledge leads to superior outcomes. I actively mentor junior team members, conduct thorough quality reviews, and champion engineering best practices across the team. I believe that the quality of the final product is a direct reflection of the team's cohesion and skill.

My experience at Softaims has refined my ability to effectively communicate complex technical concepts to non-technical stakeholders, ensuring project alignment from the outset. I am a strong believer in transparent processes and iterative delivery.

My main objective is to foster a culture of quality and accountability. I am motivated to contribute my expertise to projects that require not just technical skill, but also strong organizational and leadership abilities to succeed.

Main technologies

  • Cloud, Frontend and AI Platforms

    14 years

  • React

    13 Years

  • Django

    8 Years

  • HTML5

    3 Years

Additional skills

Direct hire

Potentially possible

Previous Company

eGeneration

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Experience Highlights

RescueVision: AI-Powered Search & Rescue Command Center

I built RescueVision, an end-to-end, multi-modal AI system to accelerate search and rescue (SAR) operations. I trained a YOLOv8n object detection model on over 2,200 aerial images, achieving a Precision of 85.3%, Recall of 76.2%, and mAP50 of 0.833. The system also includes a private RAG pipeline built with LangChain and ChromaDB. The entire project was developed with a robust microservices architecture, integrating Flask-based AI services with a React/TypeScript frontend.

AI-Powered Customer Feedback Platform

Built an end-to-end, full-stack platform to transform unstructured customer feedback into actionable business intelligence. I developed an advanced Retrieval-Augmented Generation (RAG) pipeline using Sentence-Transformers for embeddings and a persistent ChromaDB knowledge base (10,000+ items). The platform features Intelligent Document Processing with LLM-based chunking and Tesseract OCR for comprehensive data ingestion (.pdf, .docx). The Groq LPU-powered LLaMA 3 model provides high-speed conversational analysis (average time-to-first-token <150ms).

Prescriptive Maintenance System (ML, LLM, RAG, NLP)

I built a Prescriptive Maintenance system that goes beyond prediction by using a Retrieval-Augmented Generation (RAG) pipeline to prescribe solutions via a conversational AI assistant. It includes an end-to-end MLOps lifecycle, a Human-in-the-Loop (HITL) framework, and Docker containerization. I achieved an RMSE of 15.82 and 95% Recall. The system's private RAG pipeline uses Ollama, ChromaDB, and LangChain.

AuraScanAI: AI-Powered Vehicle Damage Assessment System

I built AuraScanAI, an end-to-end computer vision system demonstrating a full MLOps lifecycle for vehicle damage assessment. I custom-trained a Vision Transformer (ViT) on over 15,500 images, fine-tuning a vit_base_patch16_224 model to achieve a best validation loss of 248.27. The system features a professional MLOps workflow using Docker and Git LFS for model management, and a full-stack architecture with a Flask/PyTorch API on Hugging Face Spaces and a React/TypeScript frontend on Vercel. The backend also includes a Business Rule Engine for severity classification and repair costs.

AI Research Assistant (Local LLM & RAG Agent)

Developed a full-stack, end-to-end AI agent capable of answering complex questions with up-to-date, sourced information. This project utilizes modern AI agent architecture, achieving a 100% task success rate and 67% faster responses by leveraging a robust, orchestrated Retrieval-Augmented Generation (RAG) workflow with a local LLM (Phi-3).

Education

  • International University of Business Agriculture and Technology

    Bachelor of Science (BS) in Electrical and Electronics Engineering

    2010-01-01-2014-01-01

Languages

  • English

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