I am a Software Engineer specializing in Artificial Intelligence and Machine Learning, with hands-on experience in developing practical, real-world AI and ML solutions. I am passionate about leveraging data-driven approaches to solve complex problems and focus on areas including Machine Learning, Deep Learning, Artificial Intelligence, and Data Science. I actively work on projects that deliver impactful, real-world applications while continuously expanding my knowledge and skills in the field.

🏅 PEC Level-2 Certified Software Engineer

Deep Learning & NLP: BERT, DeBERTa, CNNs, RNNs
LLMs & GenAI: LangChain, Ollama, Groq API

🔹 Automated Essay Scoring 2.0

Problem: Manually checking student essays takes a lot of time and can be biased.
Approach & Technologies: Built an AI system using Python and NLP models to automatically score essays. Used text preprocessing, transformer models, and supervised learning.
Outcome: Created an end-to-end essay scoring system that reduces manual grading effort.
🔗 GitHub: https://github.com/engrsabakhan/Automated-Essay-Scoring-2.0

🔹 RAG Chatbot
Problem: Normal chatbots give general answers and do not use stored data.
Approach & Technologies: Built a Retrieval-Augmented Generation (RAG) chatbot using Python and LLMs that first searches documents and then answers questions.
Outcome:The chatbot gives more accurate and relevant answers based on documents.
🔗 GitHub: https://github.com/engrsabakhan/Rag-Chatbot

🔹 AI-Powered Cold Email Generator (Job & Client Outreach)
Problem: Writing cold emails for jobs or clients is slow and repetitive.
Approach & Technologies: Used AI and LLMs to generate personalized emails by analyzing job descriptions and user details.
Outcome:Saves time and helps send professional, customized emails.
🔗 GitHub: https://github.com/engrsabakhan/AI-Powered-Cold-Email-Generator-Job-Client-Outreach

🔹 AI Image App
Problem: Creating AI images is difficult for non-technical users.
Approach & Technologies: Built a Streamlit web app using Hugging Face image generation models to create images from text prompts.
Outcome: Users can easily generate and download AI images through a simple interface.
🔗 GitHub: https://github.com/engrsabakhan/ai_image_app

🔹 Child Mind Institute – Problematic Internet Use
Problem: Excessive internet use can negatively affect children’s mental health.
Approach & Technologies: Analyzed data using Python to study patterns of problematic internet usage.
Outcome: Provided useful insights into internet behavior and its impact on children.
🔗 GitHub: https://github.com/engrsabakhan/Child-Mind-Institute-Problematic-Internet--used

🔹 Role: Machine Learning Intern

✨ Highlights:

🧠 Designed complete ML workflows covering preprocessing, feature engineering, training, and evaluation.

📝 Built an AI-powered essay scoring system using Python, TensorFlow, Scikit-learn, Groq, and Streamlit.

⚙️ Optimized and validated models for real-world deployment.

📊 Prepared technical documentation and shared findings with mentors for transparent experimentation.

🌐 Web Development Internship ( EzeeSol Technology Rawalpindi June 2024 – August 2024)

🔹 Role: Web Developer Intern

✨ Highlights:

💻 Assisted in front-end and back-end development of production-level web applications.

🎨 Improved user interfaces and application flow through collaborative development.

🛠️ Strengthened practical skills in HTML, CSS, JavaScript, and PHP.

         🔹 Research Area: Computer Vision, Medical Imaging
         🔹 Methods: YOLO-based Deep Learning, 3D Tomographic Analysis
         🔹 Status: Academic Research Paper

         🔹 Research Area: Computer Vision, Machine Learning
         🔹 Methods: Support Vector Machines (SVM), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN)
         🔹 Status: Published

         🔹 Research Area: Digital Twin Technology, Biomedical Signal Processing
         🔹 Methods: Sparse Identification of Nonlinear Dynamics (SINDy), Physics-Informed Neural Networks (PINN)
         🔹 Status: Draft Academic Research

         🔹 Research Area: Artificial Intelligence, Healthcare Analytics
         🔹 Methods: CatBoost, XGBoost, LightGBM, Ensemble Learning
         🔹 Status: Final Academic Research Manuscript (Unpublished) — IEEE Format

         🔹 Research Area: Natural Language Processing, Educational AI
         🔹 Methods: Linear Regression, XGBoost, LightGBM, LSTM, BERT
         🔹 Status: Academic Research Paper

         🔹 Focus Area: Blockchain, Artificial Intelligence, IoT, 6G Networks
         🔹 Objective: Ensuring real-time semantic data integrity and secure access control using a hybrid Blockchain-AI architecture.

         🔹 Focus Area: Energy Analytics, Artificial Intelligence, Behavioral Data
         🔹 Objective: Predict electricity consumption patterns and classify consumers based on behavioral indicators to optimize billing.

         🔹 Focus Area: Healthcare AI, NLP, Patient Management
         🔹 Objective: Develop an AI-driven triage system to streamline patient prioritization and improve healthcare response efficiency.

         CZII – CryoET Object Identification: Ranked 536 / 931
         ISIC 2024 – Skin Cancer Detection with 3D-TBP: Ranked 2597 / 2739
         BirdCLEF 2024: Ranked 333 / 974
         Learning Agency Lab – Automated Essay Scoring 2.0: Ranked 2137 / 2706

         Machine Learning Intern – Ezitech Institute
         Web Development Intern – EzeeSol Technologies
         Machine Learning & Data Science Intern (Demo Training Program) – Edureka

         Active contributor on Kaggle, participating in competitions across ML, NLP, medical imaging, and audio classification.
         Continuously learning and refining skills in data science, AI, and real-world ML applications.

• ✉️ Email: [email protected]
• 🔗 LinkedIn: To be confirmed
• 🐙 GitHub: github.com/engrsabakhan
• 🏆 Kaggle: https://www.kaggle.com/sabakhanzadi