I am an AIOps Engineer at Zurich North America, where I build scalable ML and observability frameworks that enhance automation, monitoring, and operational efficiency across enterprise systems. My expertise includes MLOps, Azure Databricks, AIOps frameworks, and advanced language models, with a focus on translating research into impactful, real-world solutions. Previously, I worked as an MLOps Engineer at CGI, a Machine Learning Engineer at Infosys, and a Data Science Intern at GM Financial, where I leveraged Azure Databricks and Ray to design and scale ML workflows. These roles strengthened my background in data engineering, cloud platforms, and end-to-end ML pipelines. Alongside my industry contributions, I actively engage in AI and ML research at the intersection of automation, trust, and emerging technologies, combining academic insights with practical engineering. I also volunteer as the Vice Chair of ACM Dallas, supporting professional growth and collaboration within the AI community.