AI/ML Engineer and Data Scientist

I am a data scientist and AI/ML engineer focused on building predictive models, automated MLOps pipelines, and LLM-powered retrieval systems. I combine statistical foundations with practical engineering to deliver data products that support informed business decisions.

I work end to end across the machine learning lifecycle, from data cleaning, exploration, modeling, and forecasting through API deployment, containerization, CI/CD, and cloud infrastructure. My core stack includes Python, SQL, FastAPI, Docker, AWS, and PostgreSQL.

In my current data science and analytics work, I build Power BI KPI dashboards, structured data models, and ETL workflows for management reporting and forecasting. I also develop integrations that transform nested API data into usable reporting datasets.

I have hands-on experience with machine learning methods including XGBoost, LSTM, customer churn prediction, campaign response prediction, and reinforcement learning approaches such as PPO and DQN. I am particularly interested in applying these methods to forecasting and operational decision-making.

I also build LLM and RAG applications using LangChain, Google Gemini, embeddings, Pinecone, and FastAPI. My work includes document ingestion, vector search, and grounded-answer chatbot backends.

I am currently pursuing an MSc in Artificial Intelligence and Machine Learning, researching reinforcement-learning-enhanced demand forecasting. I hold a BSc in Statistics and Financial Mathematics and am open to remote AI/ML, data science, and MLOps opportunities.