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Md. Ehsanul Haque K. - Fullstack Developer, React Native, nextjs

Working at Softaims has been an experience that continues to shape my perspective on what it means to build quality software. I’ve learned that technology alone doesn’t solve problems—understanding people, processes, and context is what truly drives innovation. Every project begins with a question: what value are we creating, and how can we make it lasting? This mindset has helped me develop systems that are both adaptable and reliable, designed to evolve as business needs change. I take a thoughtful approach to problem-solving. Instead of rushing toward quick fixes, I prioritize clarity, sustainability, and collaboration. Every decision in development carries long-term implications, and I strive to make those decisions with care and intention. This philosophy allows me to contribute to projects that are not only functional, but also aligned with the values and goals of the people who use them. Softaims has also given me the opportunity to work with diverse teams and clients, exposing me to different perspectives and problem domains. I’ve come to appreciate the balance between technical excellence and human-centered design. What drives me most is seeing our solutions empower businesses and individuals to operate more efficiently, make better decisions, and achieve meaningful outcomes. Every challenge here is a chance to learn something new—about technology, teamwork, or the way people interact with digital systems. As I continue to grow with Softaims, my focus remains on delivering solutions that are innovative, responsible, and enduring.

Main technologies

  • Fullstack Developer

    14 years

  • React

    6 Years

  • Django

    4 Years

  • HTML5

    3 Years

Additional skills

  • React
  • Django
  • HTML5
  • On-Page SEO
  • Technical Writing
  • Yoast SEO
  • Blog Writing
  • Machine Learning
  • Mobile UI Design
  • User Experience Design
  • Artificial Intelligence
  • React Native
  • nextjs

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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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