Machine Learning Engineer • Building AI Systems for Real-World Impact Focused on production-ready AI, applied research, and intelligent applications

I am a Computer Science (AI & ML) student and applied machine learning practitioner focused on designing, building, and deploying real-world AI systems.

My work emphasizes model development, interpretability, data-driven decision-making, and scalable AI solutions, with applications in healthcare, automation, and environmental intelligence.

I prioritize depth over hype, clarity over noise, and real execution over surface-level experimentation.

• Building production-ready machine learning systems
• Developing end-to-end ML pipelines (data → training → evaluation → deployment)
• Applying deep learning to healthcare and real-world problem domains
• Learning Cloud Engineering, MLOps, and scalable AI infrastructure
• Integrating AI models into real applications and full-stack systems
• Advancing research in AI for Healthcare & Environmental Sustainability

A deep learning-based medical imaging system using PyTorch, Grad-CAM explainability, and Streamlit, focused on interpretability and clinical relevance.

An end-to-end machine learning pipeline built using real-world Telco data, including EDA, modeling, evaluation, and deployment.

Research-driven work exploring AI-powered analytics and sustainability-focused problem solving.

A next-generation AI-powered design-to-development platform enabling real-time synchronization between design and production code.

• Python
• JavaScript
• SQL
• R

• PyTorch
• TensorFlow
• scikit-learn
• Pandas, NumPy
• Matplotlib
• MLflow
• Power BI

• Node.js
• Express.js
• Next.js
• MongoDB
• MySQL
• HTML5, CSS3, Bootstrap
• Streamlit

• Git & GitHub
• Docker (learning)
• CI/CD (learning)
• Cloud Platforms (AWS / GCP — learning)

• LinkedIn: https://linkedin.com/in/mohammed-yaseen-843638223
• Instagram: https://instagram.com/_.yaseen22
• Email: mmohammedyaseen87@gmail.com

Focused on mastery • Building meaningful AI • Thinking long-term