2004-2012
Film and theatre at Hanyang
Studied cinematography, film directing, sound, and editing before software became the craft.
About Brandon
I came from film, stayed for systems, and now build toward AI infrastructure.
Product engineer in Seoul, co-leading backend at Moba and sole-maintaining the calendar sync system across backend, infrastructure, and production recovery work.
I started in film and theatre, moved into software in 2019, and grew through frontend, full-stack, backend, DevOps, and AI-native engineering work.
The through-line is not a title. It is the way I work: trace the system, name the tradeoffs, verify with tests and logs, then write down what changed my mind.
career arc
2019-2021
Self-study into engineering
Built a foundation through JavaScript, Java, C, Nand2Tetris, algorithms, and production-oriented web work.
2021-2025
Frontend to backend ownership
Worked across MODULABS, Moviation, and Playtag, moving from product UI to full-stack delivery, service migration, and production operations.
2025-now
Moba backend, DevOps, and AI data systems
Sole maintainer for calendar sync while also owning billing surfaces, real-time updates, AWS/Terraform infrastructure, and Airflow-based data pipelines.
what I build now
The current work is less about one backend feature and more about the product system that has to keep operating.
Calendar sync at product scale
Took a fragile single-calendar, one-year sync path into full Google/Apple multi-account and multi-calendar sync across 7M events.
Infrastructure that the team can operate
Terraform, GitHub Actions, ECS, RDS, S3, DynamoDB state, WAF hardening, and deployment paths that fail visibly.
Data systems moving toward AI
Airflow ingestion, Amplitude ETL completeness, product analytics, and the path from local prototypes to production pipelines.
personal systems
Brandon's Binary Brain
3B
A version-controlled personal operating system for notes, agent rules, skills, reviewer loops, and decision memory.
AI knowledge platform
Crucio
A personal AI system for model experimentation, knowledge workflows, and pipeline inspection.
public learning surface
brandonwie.dev
The place where production notes become essays after the evidence is strong enough to share.
operating principles
Correctness before cleverness
Timezones, recurrence rules, soft deletes, orphan rows, and race conditions deserve boring precision.
Evidence beats confidence
I use AI heavily, but decisions still need tests, logs, diffs, production data, and explicit ownership.
Write the tradeoff down
Good systems work is remembering why a decision was reasonable when the context is no longer fresh.
Building toward
MLOps and AI engineering, without pretending the work is already done.
The next edge is deeper computer science and AI systems work: graduate-level foundations, production ML infrastructure, and tools that make agents easier to verify.
- Georgia Tech OMSCS target: Spring 2027
- GTx math and algorithms certificates
- AWS Developer and Solutions Architect track
- Operating systems, concurrency, and lower-level systems depth