I am an aspiring Applied Machine Learning / AI Engineer and a Computer Science & Design undergraduate at SUTD. I focus on bridging the gap between theoretical machine learning models and production-ready software systems. Projects I have explored include custom model fine-tuning, parameter optimisation, and agentic workflows.

Always looking out for opportunities!

• Machine Learning & MLOps: Fine-tuning open-weights architectures (LoRA/QLoRA), model quantization engineering (GGUF via llama.cpp), and context-window optimization.
• Agentic Workflows: Designing state-persistent multi-agent architectures, structured evaluation loops, and deterministic human-in-the-loop guardrails.
• Full-Stack AI Software: Building asynchronous web microservices (FastAPI), secure backend state management, and containerized deployment infrastructure (Docker).

• Languages: Python, Java, SQL, C, Kotlin, JavaScript
• AI/ML Infrastructure: PyTorch, Unsloth, Hugging Face (PEFT/Transformers), LangChain, LangGraph, LangSmith
• Backend & DevOps: FastAPI, Docker, Uvicorn, Firebase, SQLiteCloud, Node.js

• 🧠 LoRA-Injection-Detector: Fine-tuning an 8B Llama model on adversarial partitions and compiling it down to a 4-bit CPU GGUF microservice container.
• 🏥 Agentic AI in Healthcare (Research): Co-leading the evaluation framework for a coordinated multi-agent architecture in medical conversation contexts.