Architected and built an end-to-end Generative AI platform from zero — LLM orchestration, RAG pipelines, and a production React/TypeScript frontend with Python (FastAPI) and Azure Functions serverless backend

Designed and implemented responsible-AI validation layers including content-safety filters, bias-detection checks, and compliance guardrails ensuring all AI-generated outputs meet regulatory and ethical standards

Built AI safety and IP compliance systems including audit logging, provenance tracking, and output attribution pipelines — ensuring traceable and accountable AI output lifecycles

Developed prompt-engineering and LLM evaluation frameworks using Azure OpenAI GPT-4o and LangChain, with systematic fine-tuning to optimise domain accuracy across compliance use cases

Designed a cloud-based batch data processing pipeline using Microsoft Azure Batch and Docker for urban mobility analytics

Automated Computer Vision and Bluetooth sensor workflows enabling scalable, real-time smart city insights

Delivered production analytics dashboards adopted by city government and transport stakeholders

Developed a complete English–Myanmar machine translation pipeline built on a 10K gold-labeled WikiHow parallel corpus manually created and curated

Covered full research lifecycle: dataset construction, model training, evaluation design, and deployment with multilingual MT backbones

Building a public translation API and Chrome extension for real-time English–Myanmar translation on WikiHow pages

Collaborated with a team to develop an innovative NLP paraphrasing tool, enhancing user experience

Cleaned and organized datasets, optimizing training processes and improving model accuracy by 15%

Gained hands-on experience in backend operations, strengthening technical skills in data management and R&D methodology