Leveraged Large Language Models (LLMs) to generate coherent and context-aware medical diagnostic reports, improving diagnostic accuracy by up to 25%—contributing to faster, more reliable clinical decision-making.

Fine-tuned YOLO models for real-time object detection on drone imagery, enhancing media analysis pipelines by 30%—demonstrating proficiency in high-performance visual AI systems adaptable to healthcare environments.

Developed a CNN+SIFT hybrid model for face mask detection in public settings during COVID-19, achieving 95% detection accuracy and supporting public health safety efforts.

Engineered spatial models to monitor and forecast air quality using real-time sensor data, improving environmental prediction accuracy by 15%—valuable for assessing patient risk factors in oncology.

Collaborated with cross-functional teams to integrate scalable machine learning solutions into existing infrastructures, increasing system performance by 20% and ensuring interoperability in dynamic clinical environments.

Led AI projects from ideation to execution, resulting in a 20% improvement in operational efficiency and tangible business value.

Utilized Langgraph, Langflow, and Langsmith to build responsive agents and chatbots, improving interaction accuracy by 30%, reducing development time by 40%, and cutting operational costs by 20%—tools well-suited for streamlining oncology workflows and patient communication.

Developed and deployed advanced NLP models powering intelligent chatbots and voice assistants, increasing patient engagement and satisfaction by 30% through more natural and responsive interactions.

Mentored a team of machine learning engineers, cultivating a culture of innovation and accountability, resulting in a 15% improvement in project turnaround times.

Created an AI-powered chatbot tailored to address religious queries, helping over 10,000 users monthly—demonstrating a deep respect for accessibility, personalization, and diverse user needs.

Fine-tuned LLMs (e.g., T5 Small) with Chain of Thought reasoning to enhance support chatbots, improving member interaction accuracy by 10%.

Built and deployed scalable AI features using AWS (S3, EC2), reducing infrastructure costs by 15% while maintaining high performance.

Created conversational AI agents with tools like LangChain, LangSmith, and LangFlow—cutting development time by 40% and improving responsiveness by 30%.

Applied deep learning to improve decision-making and personalization across the FloatMe platform, supporting smarter financial planning for users.

Collaborated cross-functionally to ensure ML systems integrated seamlessly into production, increasing reliability and impact on day-to-day user experience.