AI Engineer

AI Engineer with strong experience building production-grade ML and LLM systems. Specialized in RAG pipelines, semi-supervised learning, time-series modeling, and containerized end-to-end ML workflows. Brings solid MLOps expertise including CI/CD, MLflow, Docker, Kubernetes, and cloud deployment. Delivered scalable ML pipelines, automated data labeling processes, and significantly improved model accuracy and delivery speed. Holds a completed Master’s degree in Data Science with a focus on industrial ML applications.

Developed a containerized end-to-end ML pipeline for time-series data including automated ingestion, feature engineering, model training, and visualization. Implemented a semi-supervised ML approach for high-frequency time-series data achieving 90.7 percent classification accuracy. Automated a computer-vision-based data labeling workflow using OpenCV reducing manual annotation effort by 64 percent. Built CI/CD pipelines using Jenkins and GitHub Actions standardizing deployments and accelerating ML delivery by 40 percent. Improved anomaly detection accuracy by 23 percent using clustering and decision-tree models. Designed a custom RAG system with FAISS, LangChain, and OpenAI API achieving over 30 percent improvement in answer relevance. Developed backend services, REST APIs, and dashboards for industrial process monitoring and KPI analysis.