AI Engineer focused on building production-ready machine learning systems
I design and ship applied AI systems across NLP, computer vision, and automation. My strength is not training models in isolation, but turning messy ideas into working systems under real constraints like cost, latency, and limited compute.
I use modern AI tooling to move fast, but I stay opinionated about architecture, evaluation, and failure modes.
- End-to-end ML systems from data ingestion to deployment
- Retrieval-augmented generation pipelines and document intelligence
- Face recognition and video processing systems under constrained resources
- ML infrastructure glue: batching, orchestration, monitoring, and automation
I care about systems that survive outside notebooks.
-
Applied NLP and LLM Systems
RAG design, prompt evaluation, embedding strategies, and retrieval tradeoffs -
Computer Vision Systems
Face detection, tracking, embedding pipelines, and large-scale similarity search -
Production ML Engineering
Dockerized services, async pipelines, cloud deployment, and cost-aware design
Python • PyTorch • Hugging Face • LangChain • Docker • AWS • Next.js
I do not reinvent everything from scratch. I integrate, debug, and optimize until the system works reliably.
- B.Sc. in Computer Science, Assumption University of Thailand
- GPA: 3.7
- Hands-on experience across NLP, generative AI, and applied ML systems
I’m early in my career, but I bias toward building things that run, fail, and improve over time.
Portfolio: https://thadoe.me
Email: [email protected]
GitHub: https://github.com/MyBoringFacts
LinkedIn: https://linkedin.com/in/thadoe
AI is not the goal. Reliable systems are.