I am an AI and Data Science Engineer with a strong focus on machine learning, deep learning, generative AI, and MLOps. I enjoy building end-to-end intelligent systems that move from experimentation to production with reliability and measurable impact.

My background includes supervised and unsupervised learning, computer vision, text analytics, statistics, and probability, along with practical experience in model development, evaluation, and interpretability. I have worked with modern AI stacks such as TensorFlow, Keras, PyTorch, Scikit-learn, LangChain, LangGraph, and Hugging Face.

I am particularly interested in GenAI architectures, multi-agent systems, RAG pipelines, and LLM fine-tuning. I like designing solutions that are not only technically advanced but also useful in real business contexts, especially where automation, decision support, and user experience matter.

I also have hands-on experience with MLOps and deployment practices, including Docker, FastAPI, CI/CD, MLflow, DVC, monitoring, and data quality validation. I value clean engineering practices and enjoy working on systems that can be maintained, scaled, and improved over time.

My experience includes projects and internships in banking, healthcare, public-sector analytics, and educational technology. These experiences helped me strengthen both my technical skills and my ability to adapt AI solutions to different domains and constraints.

I am a product-oriented and rigorous person who enjoys solving complex problems, working in collaborative environments, and continuously learning. I am open to opportunities where I can contribute to innovative AI products and industrial-grade data science solutions.