I work at the intersection of Data Science, Machine Learning, and AI Engineering, with a strong interest in building intelligent systems that can move from experimentation to production.

I enjoy understanding not just models, but also the engineering, infrastructure, and system design principles that make real-world AI applications work reliably.

Currently focused on deepening my understanding of machine learning systems, generative AI, and production engineering while continuously building hands-on projects.

• Machine Learning Engineering
• Neural Networks and Deep Learning
• NLP and Large Language Models
• Retrieval Augmented Generation (RAG)
• Generative AI Applications
• AI Agents and Agentic Workflows
• MLOps and Production Systems
• Cloud Infrastructure for AI Systems

• Data Science and Machine Learning
• Predictive Modeling and Feature Engineering
• Statistical Analysis
• Applied Machine Learning Workflows
• Building AI-powered applications
• End-to-end experimentation and deployment workflows
• Developer environment and systems fundamentals

• Advanced Python Engineering
• Machine Learning System Design
• Docker and Containerization
• FastAPI and API Development
• AWS for Machine Learning Systems
• Monitoring and Deployment Workflows
• Productionization of AI Systems

Python | SQL | R

Pandas | NumPy | Matplotlib | Seaborn | Statistics | Data Analysis

Scikit-learn | XGBoost | Feature Engineering | Model Evaluation | Predictive Modeling

LLMs | RAG | Embeddings | Prompt Engineering | AI Agents

Git | GitHub | VS Code | Jupyter Notebook | API Development | Docker (Learning) | FastAPI (Learning)

AWS (Learning)

I believe strong AI systems are built not only through good models, but also through strong engineering, reproducible workflows, and a deep understanding of the systems running underneath the code.