class MuhammadAsadKhan:
def __init__(self):
self.role = "Machine Learning Engineer "
self.university = "NUML Islamabad — Software Engineering"
self.internship = "DecodesLabs — AI /ML Intern (completed)"
self.interests = ["ML Pipelines", "LLMs", "GenAI", "Model Deployment"]
self.domains = ["Healthcare", "Education", "Data Analysis"]
self.email = "asadkhans2310861@gmail.com"
self.goal = "Build AI systems that solve real problems"
def currently_learning(self):
return ["RAG Systems", "FastAPI + Docker", "MLOps", "LLM Fine-tuning"]| 🏢 Company | 📌 Role | 📅 Status |
| DecodesLabs | Machine Learning Intern | 🟢 Ongoing |
- Building and deploying end-to-end ML and web application projects
- Developing Flask-based REST APIs to serve model predictions in production
- Working full-stack: Python backend · Firebase cloud · TypeScript frontend
| 🔬 Project | 📝 Description | 🛠️ Stack | 🔗 |
|---|---|---|---|
| SVM Digit Classifier | Handwritten digit recognition — ~98% accuracy with full pipeline, 8 custom visualizations, model persistence | Python · Scikit-learn · Seaborn | → View |
| ElectraGuard | Intelligent full-stack security system with ML-powered API backend | TypeScript · Python · Flask | → View |
| Electra-API | REST API serving real-time ML predictions for ElectraGuard | Python · Flask | → View |
| Deep Learning Projects | Collection of DL experiments and model implementations | Python · TensorFlow · Jupyter | → View |
| Iris ML Model | End-to-end classification pipeline with training & model persistence | Python · Scikit-learn | → View |
Languages & Core
ML / AI / Data Science
Web, Backend & Cloud
Tools
- 🔗 RAG Systems — Retrieval-Augmented Generation with LLMs
- 🐳 MLOps — Model deployment with FastAPI + Docker
- 📊 MNIST & large-scale datasets — scaling beyond toy problems
- 🧪 LLM Fine-tuning — domain-specific model adaptation
Open to junior ML engineer roles, research collaborations, and internship extensions. Feel free to reach out — let's build something meaningful together.