AI Engineer with hands-on experience building LLM-powered applications, including RAG pipelines, conversational agents, and NLP systems. Skilled in Python, Hugging Face, and PyTorch, with a focus on developing practical, production-ready AI solutions. Strong interest in model optimization, scalable deployment, and real-world AI applications.
• Developed an AI-driven image classification system using Python to automate oral cancer detection from medical imagery. • Implemented data preprocessing pipelines and API connectors to streamline the flow of medical data into analysis modules. • Configured system guardrails to maintain high accuracy and data privacy standards for sensitive medical diagnostics. • Updated performance tuning parameters to ensure low-latency processing for real-time diagnostic applications. • Collaborated on the integration of data access frameworks to manage large-scale medical image repositories. • Delivered detailed technical reports on AI model performance and validation metrics for cross-functional review.
• Built an automated data extraction engine utilizing Natural Language Processing (NLP) techniques to transform unstructured resumes into structured data. • Implemented logic inspired by Retrieval-Augmented Generation to match candidate profiles against specific job requirements for automated screening. • Created Python modules and REST APIs to facilitate the flow of data between frontend and extraction layers. • Tested parsing reliability across diverse document layouts to ensure consistent performance for enterprise usage. • Applied dependency injection to improve the modularity and maintainability of the parsing engine's backend components. • Designed visualization dashboards to monitor extraction accuracy and identify areas for model refinement.