AI Engineer | M.Eng. in AI Engineering for Autonomous Systems
Specializing in Large Language Models, Computer Vision & Autonomous Systems

I'm an AI Engineer with expertise in LLMs, Deep Learning, Computer Vision, and Machine Learning. I love building intelligent systems that solve real-world problems and pushing the boundaries of what's possible with AI.

🎓 Currently pursuing M.Eng. in AI Engineering for Autonomous Systems
💼 Professional experience in LLMs, Data Engineering, and Predictive Analytics
🌱 Always learning and exploring new technologies in AI and ML
📍 Based in Germany | Fluent in English & German (C1)

• 🤖 AI & Machine Learning: Building and fine-tuning neural networks, working with LLMs and transformer models
• 📊 Data Science: Data pipeline development, statistical analysis, and visualization
• 🚗 Computer Vision: Object detection, scene understanding, and autonomous systems
• 🧠 Deep Learning: PyTorch, TensorFlow, Reinforcement Learning (Q-Learning, DQN)
• ⚙️ Engineering: Automation, production-ready ML systems, currently exploring MLOps and CI/CD

Languages & Frameworks:

Data & Analytics:

Tools & Technologies:

Deep Learning Architectures:

• 🔷 CNNs (Convolutional Neural Networks) - Image processing & computer vision
• 🔁 RNNs (Recurrent Neural Networks) - Sequential data & time series
• 🔄 LSTMs (Long Short-Term Memory) - Long-term dependencies & memory
• ⚡ Transformers & Attention Mechanisms - State-of-the-art sequence modeling

Advanced AI Models:

• 🤖 LLMs (Large Language Models) - Text generation & understanding
• 👁️ VLMs (Vision-Language Models) - Multimodal understanding
• 🎯 RL Agents (Q-Learning, DQN) - Decision-making systems

skills = {
    "AI & ML": ["Deep Learning", "Neural Networks", "LLMs", "Computer Vision", "NLP"],
    "Frameworks": ["PyTorch", "TensorFlow", "Keras", "Transformers", "OpenCV"],
    "Data Science": ["Data Analysis", "Statistical Modeling", "Feature Engineering"],
    "MLOps": ["Model Training", "Evaluation", "Optimization", "Deployment"],
    "Tools": ["Git", "Docker", "Linux", "Jupyter", "Power BI", "ROS2"]
}

Specialized in fine-tuning large language models using parameter-efficient techniques (LoRA/PEFT) for computer vision tasks, achieving significant performance improvements over baseline models.

Developed custom environments and implemented RL algorithms including Q-Learning and Deep Q-Networks (DQN) for autonomous agent training and decision-making systems.

Built automated data pipelines and KPI dashboards that improved operational efficiency. Expertise in processing large-scale datasets and extracting actionable insights.

Applied deep learning to urban traffic analysis and autonomous driving perception tasks, working with sensor fusion and time-series prediction.

Built end-to-end ML pipeline with scikit-learn for housing price prediction, achieving Test RMSE of $48,749 with Random Forest and comprehensive hyperparameter optimization.

Implemented Deep Q-Network (DQN) integrated with SUMO traffic simulator for intelligent traffic light optimization, trained over 500 episodes with stable convergence.

✨ AI Research & Development - Working with cutting-edge LLMs and multimodal models
✨ Data Engineering - Building robust pipelines for production ML systems
✨ Predictive Analytics - Condition-based maintenance and reliability optimization
✨ Automation - Creating automated reporting systems and workflows

I'm passionate about staying at the forefront of AI technology:

• 📖 Exploring latest research in LLMs and transformer architectures
• 🧪 Experimenting with new ML frameworks and tools
• 🤝 Contributing to open-source projects
• 💡 Sharing knowledge and learning from the community

I'm always interested in:

• 💬 Discussing AI, ML, and Data Science projects
• 🤝 Collaborating on innovative solutions
• 📚 Sharing knowledge and experiences
• 🌟 Exploring new opportunities

Reach out to me:

• 📧 Email: [email protected]
• 💼 LinkedIn: muhammadshariqkhan
• 🐙 GitHub: @muk0644

💡 "Transforming data into intelligence, one model at a time."