Professional Overview
I'm an early-career AI Engineer experienced in RAG systems, AI chatbot development, data analytics, and technical research. I build AI-assisted workflows, design chatbot logic, analyze data, and create clear technical documentation using Python, ML, and structured problem-solving.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Developed Dynamic RAG framework for document validity assessment
- Built AI workflow to verify document relevance and activeness
- Presented weekly research progress and technical findings
- Supported AI research lifecycle from ideation to documentation
Key Achievements / KPIs
- Built RAG-based system for technical document validation
- Supported internal standard development with AI-assisted review
- Led research execution across AI governance and assurance topics
- Delivered regular technical presentations to research stakeholders
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Designed ML and NLP training modules for AI learners
- Created chatbot project exercises and assessments
- Mentored 6 participants through AI project development
- Reviewed participant projects and gave technical feedback
Key Achievements / KPIs
- Mentored 6 AI learners in chatbot project development
- Developed structured ML and NLP learning materials
- Supported project-based learning through assessments
- Improved AI learning delivery through feedback reporting
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Conducted research on computer vision and object detection
- Trained YOLO models for small object detection research
- Prepared datasets using annotation and augmentation workflows
- Authored and presented AI research at international conference
Key Achievements / KPIs
- Published first-author IEEE paper on YOLO optimizer comparison
- Presented computer vision research at ICICyTA 2024
- Built hands-on experience with YOLO and Roboflow workflows
- Developed research capability in AI and image processing
Education
Field of Study: Signal Processing
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
AI Product & Strategy
Translate AI capabilities into product roadmaps, define success metrics, and drive adoption across engineering and business teams.
