San Gabriel, California, United States
MS CS @ USC (2026) | ML Engineer passionate about building production-ready GenAI, LLM, and multimodal AI systems at scale. I thrive at the intersection of research and engineering, designing agentic pipelines, RAG systems, LLM evaluation frameworks, and end-to-end MLOps infrastructure that actually works in production. Currently researching autonomous multi-agent LLM systems at USC's NEXDIG Lab, and previously at Autodesk (SF) where I built an LLM-as-a-Judge evaluation pipeline and AWS-deployed MLOps system for Salesforce CRM AI, cutting manual QA by 60%. I work across PyTorch, TensorFlow, LangChain, Hugging Face, AWS, Docker, Kubernetes, and Spark with deep focus on fine-tuning, hallucination evaluation, and scalable GenAI infrastructure. Actively seeking New Grad 2026 roles in ML Engineering, Applied AI, or GenAI Infrastructure, let's talk: [email protected]
• Built the core implementation of a multi-agent analytical system for automated insight discovery, covering question generation, answer execution, and validation workflows • Implemented utility scoring logic for candidate question selection based on novelty, coverage, specificity, and redundancy across insight trajectories • Integrated LLM-based reasoning with SQL/Python execution planning and automated validation for structured data analysis • Developed skill retrieval and execution planning mechanisms that select analytical skills via embedding similarity and support SQL/Python dual-engine reasoning
Taught Students AP Statistics, AP Computer Science Principles, C++, and AP Physics. Developed customized lesson plans and exercises from scratch to ensure students grasped foundational concepts. Provided ongoing support by creating assignments, reviewing homework, and preparing students for exams. Improved students' performance by fostering a deep understanding of complex topics.
Provided in-depth guidance to university students across various mathematical and statistical disciplines, including calculus, linear algebra, and probability theory. Conducted personalized, one-on-one sessions to resolve complex queries and demystify challenging concepts. Empowered students to excel academically by delivering tailored support and strategic study methodologies.
Built data models and maps to generate meaningful insights from surveys Spearheaded project to compile, study, and infer data and model information to understand root of competitor social media success and failure to better penetrate market Worked with marketing teams to identify new client program by utilizing Charles to capture data from eight Youtubers (9834 titles) for analysis Edited and strategically distributed engaging videos across major platforms Monitored YouTube metrics for optimization and analyzed video titles for keywords Applied advanced analytics (Logistic Regression, Random Forest, Neural Network) for actionable insights Performed data scrubbing, troubleshooting, quality checking, and usability Maintained a working knowledge of Data ETL, visualization, API framework, and statistical methods in Python Received commendation from ten clients attributing increased visitations to engaging content. Achieved a 45.6% surge in YouTube views, and a 32.5% increase in subscribers, showcasing impactful results