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Muhammad M. - Fullstack Developer, React Native, nextjs

My journey at Softaims has been defined by curiosity, growth, and collaboration. I’ve always believed that good software is not just built—it’s carefully shaped through understanding, exploration, and iteration. Every project I’ve worked on has taught me something new about how to balance simplicity with depth, and efficiency with creativity. At its core, my work revolves around helping businesses and people achieve more through thoughtful technology. I’ve learned that the most successful projects come from teams that communicate openly and stay adaptable. At Softaims, I’ve had the opportunity to work alongside professionals who challenge assumptions, share knowledge generously, and inspire continuous improvement. I take pride in focusing on the fundamentals—clarity in logic, consistency in design, and empathy in execution. Software is more than a set of features; it’s a reflection of how we think about problems and how we choose to solve them. By maintaining this perspective, I aim to build solutions that are not only effective today but also flexible enough to support the challenges of tomorrow. The culture at Softaims promotes learning as an ongoing process. Every new project feels like a step forward, both personally and professionally. I see each challenge as a chance to refine my skills and contribute to the shared vision of building technology that genuinely improves lives.

Main technologies

  • Fullstack Developer

    1 year

  • Software Development

    1 Year

  • SaaS Development

    1 Year

  • Web Development

    1 Year

Additional skills

  • Software Development
  • SaaS Development
  • Web Development
  • Web Application Development
  • Scripting
  • Chatbot
  • NoSQL Database
  • Relational Database
  • Django
  • React
  • Python
  • TypeScript
  • Bot Development
  • AI Development
  • Web Scraping
  • Machine Learning
  • React Native
  • nextjs

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Experience Highlights

Web Based RAG System with PDF + Google Search Q\&A

A web Flasbased RAG system for answering questions over PDF documents, enhanced with real time Google search. Built with Flask, LangChain, and Ollama-hosted LLMs, it supports PDF uploads, vector based retrieval (via Chroma), and async response generation. Google results are scraped and ranked, enriching answers with live web data. Features include real-time streaming, optional text-to-speech, responsive UI, secure file handling, and session management ensuring usability, extensibility, and robust performance.

AI-Powered Smart Contract Analyzer with LLMs & RAG Using Flask & Azure

Developed a secure AI-powered web portal for contractors to upload and analyze construction contract documents. Used Python (python-docx, PyMuPDF) for text extraction, vectorization for RAG with LLMs (DeepSeek or Llama 3.2), and Flask with HTML/CSS for the dashboard. Generated risk insights and Excel reports using pre-defined claims. Integrated chatbot for document-based queries. Deployed on Azure with a scalable backend, focusing on performance, security, and future enhancements.

OpenAI GPT4-Based Crypto Trading Chatbot with Live Swap Insights

Developed an AI-powered crypto swap assistant using OpenAI's GPT4, fine-tuned for retrieval and Q&A tasks. Built a real-time chatbot with Flask, WebSockets, and a LangChain agent for accurate, streaming word-by-word responses. Integrated data from Mempool, LiFi, DeFiLlama, and Etherscan to enhance trading insights. Refined prompt engineering for improved swap recommendations and optimized front-end UX with HTML, CSS, and JavaScript for seamless user interaction.

BOT that generate PASF in Google and train GOOGLE daily

This project builds an automation bot that mimics real user behavior to influence Google's PASF (People Also Search For) and PAA (People Also Ask) features. By using targeted short- and long-tail keywords, the bot shapes SERP results, guiding Google's perception of relevance. It performs automated searches, clicks specific links, and mimics natural actions like scrolling or pausing. Running through Incogniton browser with Oxylabs rotating proxies, it ensures anonymity while training Google daily to favor chosen keyword clusters.

Automated Goal Based Trading System with Session Control

This project sets up a structured daily trading system with customizable features. It operates on preset daily goals either profit or loss. Once a goal is hit, trading stops for that session or the entire day. Each day is split into three sessions, each with its own start/end time, balance percentage, and win/loss targets. The default time zone is UTC+3, but users can adjust it. All settings are pre-configured yet fully customizable, allowing users to adapt the system to their trading strategy and risk preferences.

Education

  • University Of Central Punjab

    Bachelor's degree in Computer science

    2020-01-01-2024-01-01

  • Division public school and college

    High school degree in ics

    2018-01-01-2020-01-01

Languages

  • English

Personal Accounts