Hello, I'm

Xiao Jun Ang

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Xiao Jun Ang
PyTorch
Python
LLMs
Deep Learning
Docker
SQL

About Me

I'm an AI Engineer with hands-on experience building end-to-end ML pipelines and deploying production-grade models across classical ML, computer vision, and Large Language Models. Currently training under AI Singapore's national AI Apprenticeship Programme (AIAP) and pursuing a Master of IT in Business (AI) at Singapore Management University.

My background in Pharmaceutical Science (NUS, Honours Distinction) gives me a unique lens — bridging rigorous scientific methodology with modern machine learning to drive data-informed decisions and scalable AI solutions.

4+ Years in Science & Data
10+ ML Tools & Frameworks
2 Graduate Degrees
GitHub LinkedIn Email

Skills & Expertise

Featured Projects

Computer Vision E-Commerce

Standardising e-commerce product images with AI-powered scoring, segmentation and background inpainting

VisionCommerce App
GitHub

VisionCommerce

AI pipeline that tackles visual fragmentation for e-commerce platforms — automatically scoring, segmenting, and standardising product images at scale.

  • ✦ Fine-tuned DINOv2 evaluates product image quality based on clarity, background, and aesthetics
  • ✦ SAM3-based segmentation generates precise product masks for targeted background editing
  • ✦ FLUX.1-FILL diffusion model inpaints consistent, high-quality backgrounds across the catalogue
PyTorch DINOv2 SAM3 FLUX.1-FILL Streamlit Computer Vision
MLOps Time Series

Predicting US flight arrival delays with end-to-end MLOps — from data ingestion to real-time monitoring

Flight Delay Map View Flight Delay Table View
GitHub Live Demo

Flight Delay Prediction

End-to-end MLOps system predicting US flight arrival delays across 3 aviation datasets, with a live Streamlit dashboard for real-time exploration.

  • ✦ Medallion architecture (Bronze/Silver/Gold) with PySpark for scalable data preprocessing
  • ✦ Airflow-orchestrated pipeline with MLflow experiment tracking and email alerting
  • ✦ Evidently AI drift monitoring with monthly evaluation cycles on production data
PySpark MLflow Airflow Docker Evidently AI Streamlit
NLP Regression Classical ML

Benchmarking job salaries with 43% better accuracy than statistical baselines using LLMs and neural networks

Job Salary Prediction — Input Form Job Salary Prediction — LinkedIn Demo
GitHub Live Demo

Job Salary Prediction

ML system for salary benchmarking trained on 34K US LinkedIn job postings, achieving R² 0.68 with an MLP — outperforming all classical baselines.

  • ✦ Gemini 2.0 Flash extracts structured requirements (skills, education, certifications) from raw job descriptions
  • ✦ SBERT embeddings (all-MiniLM-L6-v2) encode semantic job features for downstream modelling
  • ✦ MLP with Bayesian hyperparameter tuning achieves 43% MAE reduction over statistical baselines
PyTorch XGBoost SBERT Gemini scikit-learn NLP

Work Experience

AI Singapore

AI Engineer (AIAP)

AI Singapore
Jan 2026 – Present

Selected for a competitive national AI programme with intensive deep-skilling in AI/ML engineering, followed by an industry project deployment.

Python PyTorch PySpark MLflow Docker Cloud Run RAG LLMs Airflow
  • Selected for a competitive national AI programme consisting of an intensive deep-skilling phase in high-level AI/ML engineering, followed by an industry project deployment.
  • Built end-to-end ML pipelines (PySpark, MLflow, Docker, Cloud Run) supporting scalable data preprocessing, model training, evaluation, deployment, and monitoring.
  • Developed and benchmarked deep learning models (LSTM, CNN architectures) using PyTorch with systematic experimentation and hyperparameter optimization.
  • Implemented RAG-based knowledge systems combining internal datasets and external scientific literature for domain-specific question answering and decision support.

Machine Learning Engineer

Beeva AI
Sep 2025 – Jan 2026

Developed and fine-tuned deep learning models in PyTorch for computer vision, with production-grade MLOps workflows supporting reproducible research.

PyTorch Computer Vision MLflow DVC Docker Deep Learning
  • Developed and fine-tuned deep learning models in PyTorch for computer vision applications, with emphasis on generalizable feature learning and robust validation.
  • Implemented production-grade MLOps workflows (MLflow, DVC, Docker) to support reproducible research and scalable deployment.
  • Optimized models for latency, inference cost, and deployment constraints, balancing experimental performance with real-world usability.
A*STAR

Research Officer

A*STAR – Nutrition & Digestive Health
Oct 2023 – Jan 2026

Conducted PBPK/PK-PD modelling and quantitative LC-MS/MS analysis to support drug bioavailability research and evidence-based decision-making.

PBPK Modelling LC-MS/MS Python R Data Analysis ADME
  • Designed and executed in vitro biological and biochemical models (Caco-2 and SHIME®) to quantify compound bioavailability, transport, and functional performance.
  • Conducted quantitative LC-MS/MS analysis to support PBPK and PK/PD-informed models assessing pharmaco/nutrico/toxicokinetic behaviour of drugs and food ingredients.
  • Translated biological mechanisms (ADME, transporter kinetics, enzyme interactions) into model-ready parameters to improve clinical prediction accuracy.
Craft Health

Product Development Lead

Craft Health
Jun 2022 – Sep 2023

Led data-driven product development for pharmaceuticals and nutraceuticals, managing a team of five across multiple client projects.

Data Analytics Market Research Product Development Team Management
  • Led data-driven product development for pharmaceuticals, nutraceuticals, and food & beverage — translating technical and market requirements into actionable plans.
  • Conducted quantitative market research and trend analysis to identify consumer needs and generate product innovation strategies.
  • Managed a team of five formulation associates, applying structured methodologies and quality control metrics to ensure reproducibility of outcomes.

Education

Singapore Management University

Master of IT in Business – Artificial Intelligence Track

Current
  • Recipient of the Richard Lim Lee Scholarship
  • Coursework: GenAI with LLM, Applied Machine Learning, Machine Learning Engineering, Reinforcement Learning, Recommender Systems, Query Processing & Optimization, Statistical Thinking for Data Science

National University of Singapore

Bachelor of Science in Pharmaceutical Science with Honours (Distinction)

Jul 2022

Ngee Ann Polytechnic

Diploma with Merit in Biomedical Science

May 2018
  • Recipient of the Merit Scholarship Award, Overseas Merit Award, Fischer Scientific Prize, and Practical Mediscience Prize
  • Graduated in the top 10% of the Biomedical Science cohort