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About me (Registered since 03/11/2025)
My name is Muhammad Midhat, and I am a passionate AI & Full Stack Engineer with over seven years of experience developing innovative, data-driven solutions across Machine Learning, Deep Learning, Natural Language Processing (NLP), and Computer Vision (CV). I hold a Master of Science (MSc) in Computing from Universiti Malaysia Pahang, Malaysia (Remote/Research-Based, Feb 2024 – Feb 2026), where I have successfully defended my research proposal for the thesis titled “Time Series Forecasting for Industrial Machine Logs Data.” My study directly aligns with real-world industrial AI and predictive maintenance applications, and since there are no pending university tasks, I am fully available and ready for immediate relocation or remote work. I earned my Bachelor of Science in Computer Science from Government College University Faisalabad (2013–2017) and began my professional journey as a C# Developer at Microstarx Software House & Computer College (Aug 2016 – Dec 2018), where I spent 2 years and 5 months building robust desktop applications, managing SQL databases, and mentoring junior developers. Following this, I joined Inoviks Soft Solution as a Machine Learning Engineer (Feb 2019 – June 2024), where I led end-to-end AI projects, from research to deployment. My key achievements include developing deepfake detection systems, multimodal search engines, medical image segmentation pipelines, OCR-based document analyzers, and RAG-powered conversational chatbots using LLMs and vector databases. My technical skill set covers a diverse range of tools and frameworks, including Python, C#, JavaScript, SQL, TensorFlow, PyTorch, Keras, Scikit-learn, OpenCV, Detectron2, YOLOv8, Hugging Face, LangChain, AutoGen, LangGraph, and Docker. I am proficient in React.js, Node.js, Flask, and Django for web development, alongside experience in AWS, Kubernetes, and Jenkins for scalable deployment. I have also worked extensively with NLP and CV models such as BERT, DistilBERT, CLIP, U-Net, Faster R-CNN, and Mask R-CNN, applying them in projects across healthcare, manufacturing, and automation. In addition to hands-on engineering experience, I have contributed academically as a co-author of “Deep Learning-Powered Facial Expression Recognition: Revolutionizing Emotion Detection,” presented at EMSEE 2024, and as first author of a paper accepted for IEEE ICSECS 2025 titled “Literature Review: Time Series Forecasting for Text Data,” under the Faculty of Computing, Pekan, Pahang. These publications reflect my continuous effort to bridge academic innovation with industry-driven AI applications. Having built intelligent systems across domains such as predictive analytics, emotion recognition, human activity detection, and AI-driven automation, I am confident that my multidisciplinary experience and strong academic foundation make me an excellent fit for AI engineering, data science, or full-stack development roles.
Portfolio
Education
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Work Experience
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February 2019 - June 2024
Inoviks Soft Solution, Pakistan
Machine Learning Engineer
At Inoviks Soft Solution, I served as a Machine Learning Engineer responsible for designing, developing, and deploying AI-driven solutions across Computer Vision (CV), Natural Language Processing (NLP), and Generative AI domains. I led multiple end-to-end projects — from data preprocessing and model training to deployment using Docker and AWS — ensuring scalable, production-ready AI pipelines.
My key contributions include building deepfake detection systems, medical image segmentation models (U-Net, Mask R-CNN), 3D object tracking systems, and OCR-based document intelligence tools using OpenCV, Detectron2, and PyTorch. I also developed RAG-based chatbots powered by LLMs, vector databases, and LangChain, enabling intelligent document-based Q&A systems.
In NLP, I fine-tuned BERT, DistilBERT, and GPT-based models for sentiment analysis, emotion detection, and text forecasting. I collaborated with cross-functional teams to integrate AI modules into full-stack applications using React.js, Flask, and Node.js.
Through continuous optimization, innovation, and research collaboration, I enhanced model accuracy, reduced latency, and contributed to AI adoption in client-facing applications — demonstrating a strong blend of applied machine learning, research, and software engineering expertise.
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August 2016 - December 2018
Microstarx Software House & Computer College, Pakistan
C# Developer
As a C# Developer at Microstarx Software House & Computer College, I was responsible for designing, developing, and maintaining robust desktop applications using C#, .NET Framework, and SQL Server. My work focused on building efficient, user-friendly software solutions tailored to client and institutional requirements while ensuring performance, scalability, and maintainability.
I developed and optimized database-driven applications with complex data models, integrating Windows Forms, Crystal Reports, and SQL stored procedures for smooth data management and reporting. My role also included debugging, testing, and troubleshooting software modules to ensure high reliability and code quality across multiple releases.
In addition to development, I collaborated with instructors to mentor junior developers and guide students on programming best practices, database design, and object-oriented principles. I also played an active role in maintaining documentation, performing version control using Git, and deploying production-ready systems in local environments.
Through this experience, I strengthened my expertise in C#, .NET, SQL, and application architecture, laying the foundation for my transition into advanced AI and machine learning development — where software engineering and intelligent systems intersect.
