Virtual Reality Specialization

Introduction to Augmented Reality and ARCore

Finding Purpose and Meaning In Life: Living for What Matters Most

VR and 360 Video Production

MATLAB Onramp

Introduction to XR: VR, AR, and MR Foundations

Python for Everybody Specialization

C# Programming for Unity Game Development Specialization

Python Basics for Data Science | edX Verified Certificate

Computer Vision Basics

LangChain- Develop LLM powered applications with LangChain

Deep Learning with React Native & Python

Computer Vision Masterclass

Certificate of completion - 365 Data Science Program

Docker Masterclass for Machine Learning and Data Science

Shopee Code League 2021

Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs

 Deep Learning Specialization

Bachelor of Science - BS

Involved in "Place Recognition" part of Robot Navigation project.
- Collected data from the real working environment.
- Adapted SuperGlue model to do Visual Place Recognition task.
- Implemented a lot of utility files for image/video processing and running inference.
- Built a complete pipeline from raw data input to place recognition results.
- Built an end-to-end API to do Visual Place Recognition with SuperGlue and SuperPoints model using FastAPI.

AI Engineer

Deployed machine learning models to 100+ hospitals nationwide, accelerating cancer detection, to save lives.
Accomplishments:
- Enhanced existing model serving, achieving 3-4x faster inference time by resolving bottlenecks.
- Designed model serving architectures for 14+ disease prediction services (averaging 85%+ AUC), covering a wide range of model types.
- Revised Docker-based ML development platform for 35% smaller image size and 3x faster builds.
- Streamlined ML workflow under strict data privacy restrictions, boosting team efficiency and system reliability.
- Optimized CICD pipelines with Gitlab CI, automating testing, builds, and deployment resulting in faster deployment times and eliminating manual work.
- Developed a YOLOv7 model to identify artifacts in mammogram images, achieving 83% mAP@0.5. This enabled dataset cleansing for improved downstream model performance.
- Developed outlier detection models and pipelines, purging 1% of low-quality data; boosted AUC by 1.7%, sensit

Machine Learning Engineer

- Performed transfer learning with EfficientNet v2 and further adapted network structure to do tuberculosis multi-label classification.
- Performed hyperparameters tuning on the new tuberculosis classification model.
- Migrated the MLOps platform from MLflow to ClearML for tuberculosis and CXR projects.
- Wrote the new utility module for ClearML-based projects.
- Built and deployed the mammogram services (microservices) with multi-label ensemble models.
- Optimized model inference process of the mammogram services to 3-4x faster.
- Wrote well-detailed documentation and instructions about model deployment and MLOps.

ML Engineer Intern

Applied AI to build the next-generation platform for science.
Accomplishments:
- Designed and deployed a cloud-native manuscript scoring system (“Novelty Score”), enabling researchers to assess the quality and potential of their work in near real-time
- Built and operated large-scale scientific data systems using Elasticsearch and PostgreSQL, supporting backend services across 200M+ research publications in a multi-cloud environment
- Developed and maintained high-throughput data pipelines to ingest, process, and batch-analyze new open publications from OpenAlex, handling 100k+ records daily
- Introduced and implemented end-to-end MLOps practices, building a shared ML work platform to reduce iteration friction, standardize experimentation, and accelerate model deployment
- Researched and implemented an automated author email extraction pipeline from manuscripts, processing 1M+ publications to unlock scalable researcher outreach and marketing campaigns
- Deployed a RAG-based agentic LLM

AI Software Engineer

Creating and designing stock motion graphics and 3D graphics for sale

Motion Graphic Artist

Building and operating JobGlance (well, basically do everything...), a cool Chrome extension that helps job seekers instantly see whether a job is worth applying for — saving hours of scrolling through irrelevant job posts.

Focused on visa sponsorship detection, resume-to-job matching, and cross-site job tracking for global and remote job seekers.

Hint: something super cool is coming soon...

Jom Ariya

AI Engineer | Indie Hacker | Love building cool stuff that makes impact