Hi, I am
Mohammad Mujahid

Full Stack Expert

As a full stack expert, cloud specialist, and Artificial Intelligence innovator, I integrate advanced front-end design with robust back-end systems deployed on scalable cloud architectures. I harness artificial intelligence to optimize processes and create dynamic applications.
all while driving entrepreneurial ventures focused on solving mental health challenges using Artificial Intelligence and cognitive processes.
My Company: Visit our site My Resume

Skills

Frontend

React

Redux

TypeScript

AJAX

Next.js

HTML5

CSS3

JavaScript

Bootstrap

Figma

Vue.js

Angular

Sass

WebPack

Backend

Node.js

ASP.NET

Python

NPM

PHP

Java

Ruby on Rails

PostgreSQL

Sequelize

SQL

WCF

Mongodb

Cloud-Computing

AWS

Azure

Google-Cloud

Alibaba-Cloud

Git

GitHub

GitLab

Bitbucket

Docker

Jenkins

Kubernetes

Bash-Script

Ansible

Terraform

Grafana

Professional Experience

Below is a summary of my professional experience, highlighting my roles and contributions to each organization.

Tata Consultancy Services (TCS), Hyderabad, India

Full Stack Developer

December 2020 – November 2022

As a Full Stack Developer at TCS Hyderabad, I played a pivotal role in designing and implementing comprehensive business solutions. My responsibilities encompassed both client-side and server-side development, ensuring the delivery of robust and scalable applications.

Contributions:

Spearheaded the TCS OmniStore™ Implementation: Developed a dynamic e-commerce application for high-volume transactions, reengineering the backend with Node.js and Express and redesigning the frontend with React and Bootstrap.
Developed Microservices for Business Process Automation: Designed and deployed microservices using Docker and Kubernetes to automate business workflows and reduce human error.

Tasks:

Interpreted complex requirements to create detailed technical specifications for each project.
Developed resilient web services and responsive user interfaces across multiple projects.
Implemented comprehensive automated testing strategies using Jenkins to ensure robustness and reliability of software solutions.
Managed backend integrations and database management using contemporary frameworks and technologies.

Achievements:

Successfully launched the TCS OmniStore™ platform, contributing to a 30% increase in online revenue for the client.
Automated critical business processes with microservices, resulting in significant time savings and improved operational accuracy.
Recognized with the “Innovator of the Year” award for exceptional problem-solving and innovative solutions in enterprise applications.

Technologies Used: React, Angular, HTML5, CSS3, Bootstrap, TypeScript, Node.js, Express, Golang, PHP, MongoDB, MySQL, Sequelize, Jenkins, Docker, Kubernetes, AWS, Git, Bitbucket, JIRA, Webpack, npm

Future Vision LLC, Ft. Lauderdale, Florida, USA

Full Stack AI Software Engineer

January 5th – May 5th 2024

Led the development of full stack AI solutions integrating robust machine learning pipelines with dynamic web applications. Spearheaded the end-to-end design and deployment of AI models to deliver actionable insights and streamline business operations.

Contributions:

VisionNLP Suite: Designed and deployed an integrated platform that combines computer vision and NLP techniques to extract structured insights from unstructured data such as images and text. Leveraged OpenCV for image preprocessing and developed NLP pipelines using TensorFlow and PyTorch. Integrated analytics into a Jupyter dashboard and containerized with Docker and Kubernetes across AWS, Azure, and GCP. Outcome: Enhanced data extraction accuracy by 20% and reduced insight turnaround time by 25%.
Open-LLama LLM & Text-to-Image Generation Project: Leveraged open-llama open source code to fine-tune customized language models and develop advanced text-to-image generation models. Fine-tuned models on high-performance GPUs (e.g., Nvidia H100), developed pipelines using advanced diffusion models and GANs, and deployed as containerized microservices with Docker and Kubernetes. Outcome: Improved LLM performance by 18% and reduced training times by 30% while generating high-quality images from text inputs.

Tasks:

Built and optimized AI pipelines for real-time data processing and model inference.
Developed secure API endpoints for seamless integration of AI solutions into web applications.

Achievements:

Enhanced creative content capabilities and data-driven decision-making with innovative AI projects.
Achieved significant improvements in model performance and operational efficiency.

Technologies Used: Python, TensorFlow, PyTorch, Keras, Scikit-Learn, Pandas, NumPy, Jupyter, OpenCV, NLP, Docker, Kubernetes, AWS, Azure, GCP, Git, Jenkins

Freelance Full Stack Developer on Fiverr, Remote

Full Stack Developer (Full Stack & AI Developer from 2022)

2018 – 2024

Delivered end-to-end full stack solutions for diverse international clients on Fiverr. From 2018 to 2021, focused on developing scalable web applications and robust back-end architectures. From 2022 onward, strategically integrated advanced AI capabilities into these solutions, driving innovation and intelligent automation.

Contributions:

Designed and developed responsive web applications using modern front-end frameworks such as React and Angular.
Built robust back-end architectures using Node.js, Express, and integrated scalable databases.
From 2022 onward, enhanced full stack solutions by integrating AI models using Python, TensorFlow, PyTorch, and Keras.
Developed secure API endpoints to support real-time machine learning inference and data-driven decision making.
Implemented containerization and cloud deployment strategies with Docker, Kubernetes, and multi-cloud platforms (AWS, Azure, GCP).
Open-Source AI Integrated Customer Engagement Platform: Developed a comprehensive full stack solution that integrated a majority of open source AI implementations. Leveraged state-of-the-art libraries such as Hugging Face Transformers, Open-LLama, and Stable Diffusion to build conversational AI, sentiment analysis, and recommendation engines. Built custom microservices with Node.js and integrated these models seamlessly with a responsive React front-end, deploying via Docker and Kubernetes across AWS, Azure, and GCP. Outcome: Reduced development time and delivered scalable, customizable AI-driven features that significantly enhanced customer engagement and operational efficiency.

Tasks:

Architected custom solutions tailored to client needs, balancing full stack development with intelligent automation.
Optimized application performance and scalability, ensuring seamless integration of AI pipelines with web interfaces.
Conducted regular code reviews and security audits to maintain high quality and secure deployments.
Collaborated closely with clients to iteratively enhance solutions, incorporating data analytics and predictive modeling.

Achievements:

Successfully delivered multiple high-impact projects that improved operational efficiency and user engagement.
Earned repeat business and high client satisfaction by pioneering the integration of AI into full stack applications.
Recognized for reducing deployment times and enhancing application scalability through strategic innovation.

Technologies Used: JavaScript, React, Angular, Node.js, Express, MongoDB, Python, TensorFlow, PyTorch, Keras, Docker, Kubernetes, AWS, Azure, GCP, Git, Jenkins

Mastnov Tech

Founder & CEO

July 2024 – Present

Founded Mastnov Tech as a startup to explore innovative solutions for business challenges. Initially,we were wandering the company experimented with various ideas to find a viable direction. Over time, we identified a significant market need in the mental health space and pivoted to develop AI-driven cognitive solutions to address mental health issues.

Contributions:

Ideated and launched Mastnov Tech, navigating early uncertainties by exploring multiple business problem domains and identifying market gaps.
Pivoted the company’s focus to mental health, leveraging AI cognitive processes to develop solutions for emotional and psychological well-being.
Developed an AI-powered mental health platform integrating emotion recognition, cognitive behavioral therapy (CBT) techniques, and personalized support chatbots.
Implemented data-driven strategies to refine AI models for early detection of mental health issues using natural language processing and sentiment analysis.

Tasks:

Conducted extensive market research and feasibility studies to identify business problems and potential AI interventions in the mental health domain.
Designed and iterated the product roadmap, balancing initial business challenges with the eventual pivot to mental health solutions.
Led cross-functional teams to build and deploy AI models, ensuring they met regulatory and ethical standards for mental health applications.
Oversaw the integration of cognitive computing processes into user-facing applications, focusing on real-time emotional analysis and intervention.

Achievements:

Successfully navigated the early startup phase by iterating multiple business models before identifying the mental health niche.

Projects

I have worked on Full-Stack, Blockchain, and AI projects. The DevOps & Cloud Blogs are on my Tech Blog. Here are some of my projects.

OpenManus: Open-Source Autonomous AI Agent

OpenManus is an open-source initiative aimed at replicating the capabilities of the Manus AI agent, a state-of-the-art general-purpose AI developed by Monica. It autonomously executes complex tasks, including personalized travel planning, stock analysis, and more. The project provides a modular, containerized framework built with Docker, Python, and JavaScript, enabling developers and researchers to build, deploy, and experiment with multi-agent AI systems.

PythonDockerJavaScriptLangGraphFastAPINext.jsReactNode.jsGitCLIAPIWeb UIGAIA BenchmarkLLM IntegrationsWeb ScrapingVisualization

View Code

Open-LLaMA Fine-Tuning for Natural Language Reasoning

This project fine-tunes the Open-LLaMA large language model on the `facebook/natural_reasoning` dataset using Hugging Face's Transformers ecosystem. The notebook builds a complete supervised fine-tuning pipeline optimized for causal language modeling (CLM). It applies LoRA (Low-Rank Adaptation) for parameter-efficient training, Flash Attention for scalability, and bitsandbytes for 8-bit precision to enable training on limited compute. To achieve optimal performance for these compute-intensive tasks, the training was executed on high-performance Nvidia A100 and H100 GPUs, provisioned via Vast.ai GPU rentals. The goal is to enhance the LLaMA model's ability to handle complex natural language reasoning, including multi-step logic, deduction, and inference, making it more suitable for tasks like question answering, tutoring, or logical dialogue systems.

PythonOpen-LLaMAfacebook/natural_reasoning DatasetLoRA (Low-Rank Adaptation)Hugging Face TransformersSFTTrainerCausal Language Modeling (CLM)bitsandbytes (8-bit Training)Flash AttentionTokenizersPEFT (Parameter-Efficient Fine-Tuning)fp16 TrainingDatasets LibraryCUDAPyTorchGoogle Colab / Jupyter Notebooktransformers[torch]

Notebook View Code

Real-Time Identity-Preserving Face Swap (SimSwap)

This project implements a real-time, identity-preserving face swapping pipeline using the SimSwap model. Built on top of the original `neuralchen/SimSwap` architecture, the notebook automates environment setup, model checkpoint retrieval, and inference execution for seamless one-shot face swaps. It leverages ArcFace for facial identity embedding and a U-Net-like generator to blend source identity features into the target face while preserving pose, lighting, and expression. Ideal for content creators, virtual avatars, and video augmentation pipelines, this system is GPU-accelerated and tested on real human photos. The pipeline ensures reproducibility and can be adapted for video input or real-time deployment.

PythonPyTorchSimSwapArcFace EmbeddingsU-Net GeneratorReal-Time InferenceGoogle Drive CheckpointsOpenCVTorchvisionCUDAJupyter Notebook

Notebook View Code

Custom Anime Text-to-Image Generation using Stable Diffusion

This project showcases a complete pipeline for fine-tuning the Stable Diffusion model (`CompVis/stable-diffusion-v1-4`) on a domain-specific dataset: `lambdalabs/naruto-blip-captions`. Using Hugging Face's `diffusers` and `datasets` libraries, the notebook walks through dataset loading, model configuration, environment setup with Accelerate, and model training. The training pipeline leverages CLIP text embeddings and a U-Net-based denoising architecture to generate high-fidelity anime-style images from natural language prompts. Mixed-precision training and custom scheduler configurations are used for efficiency, and the process is optimized to run on NVIDIA A100/H100 GPUs using Vast.ai. This solution is ideal for creators looking to build personalized or themed image generation models in anime, gaming, or entertainment domains.

PythonStable Diffusion (v1.4)Hugging Face diffuserslambdalabs/naruto-blip-captions DatasetU-Net ArchitectureCLIP Text EmbeddingsAccelerateMixed-Precision Training (fp16)Custom Training ScriptsLoRA (if optionally added)PyTorchJupyter NotebookVast.ai (NVIDIA A100 / H100 GPUs)

Notebook View Code

Brain Tumor Classification System

The Brain Tumor Classification System is designed to assist both patients and doctors in analyzing brain MRI scans using deep learning technologies, including CNN (Convolutional Neural Network) and ResNet-50. The system provides a tailored experience for both user roles, allowing patients to securely upload MRI scans for AI-based analysis with confidence scores and comparative insights. Doctors can view detailed diagnostic reports, Grad-CAM visualizations highlighting critical areas, and access historical patient data through an intuitive analytics dashboard. The platform supports comprehensive report generation, making it a powerful tool for medical diagnostics.

FlaskMongoDBTensorFlowCNN (Convolutional Neural Network)ResNet-50Grad-CAM VisualizationJWT AuthenticationRESTful APIHTMLCSSJavaScriptBootstrapTailwind CSSEnvironment Variables (.env)Secure File UploadsDynamic Access Tokenshttps://brainseg-frontend.vercel.app/

Hi, I am Mohammad Mujahid

Skills

Frontend

Backend

Cloud-Computing

Artificial Intelligence

Professional Experience

Tata Consultancy Services (TCS), Hyderabad, India

Contributions:

Tasks:

Achievements:

Future Vision LLC, Ft. Lauderdale, Florida, USA

Contributions:

Tasks:

Achievements:

Freelance Full Stack Developer on Fiverr, Remote

Contributions:

Tasks:

Achievements:

Mastnov Tech

Contributions:

Tasks:

Achievements:

Projects

OpenManus: Open-Source Autonomous AI Agent

Open-LLaMA Fine-Tuning for Natural Language Reasoning

Real-Time Identity-Preserving Face Swap (SimSwap)

Custom Anime Text-to-Image Generation using Stable Diffusion

Brain Tumor Classification System

Mujju's AI Image Generation App

Mujjus Chess AI

Mujju Terminal for Windows

Mujju Terminal for macOS

Mujju's Search Engine

Mujju's Payment Gateway

Mujju's YouTube Video Downloader

Mujju's Python-Code-Runner

Mujju's QR Code Generator

Mujju's WhatsApp

Mujju's Ethereum Wallet

Instagram Reel Downloader

Mujjus Supply Chain DApp

Mujjus Ecommerce Store

Child-Care Management

Pet-Care Management

MUJJUS-GPT

MUJJUS Coffee Business

Education

Florida Atlantic University

ISL Engineering College, Hyderabad

Govt Polytechnic

Education

Florida Atlantic University

ISL Engineering College, Hyderabad

Govt Polytechnic

Contact Me

Hi, I am
Mohammad Mujahid