Tech Lead.
Hands-on builder.
I’m Wayne, Tech Lead at Growtrics. I lead engineering delivery and stay close to the code. My work spans autonomous QA, learning products, real-time voice, and the systems that bring them into production.
A few things
I’ve helped build.
Different products, different parts of the system. The case studies explain my contribution.
Team-product media: TaloTrace’s recorded component demo uses a fictional checkout; Academy shows its historical interface; Clevah uses official app-store screenshots.
The path
so far.
Computer Science studies and research ran alongside my early engineering work. Here’s how those chapters fit together.
Growtrics
Tech Lead · joined as AI Software Engineer
Aug 2025 – Present
Now leading engineering delivery while contributing to AI products, learning experiences, and autonomous software testing.
MUST
AI Software Engineer · full-time
Jun 2023 – Aug 2025
AI software engineering across applications and backend systems.
DevUP
AI Developer · part-time
Oct 2022 – Apr 2024
AI development alongside my full-time engineering roles.
NVIAI Smart Technology
AI Engineer · full-time
Jun 2022 – Jun 2023
Applied AI engineering.
HCMUT–VNPT Lab
AI Research Assistant
Oct 2021 – Jun 2024
University research work alongside my Computer Science studies.
How I lead.
Planning the work and helping the team deliver it, with implementation still part of my day.
Turn requirements
into releases.I break work into engineering plans, coordinate delivery, and stay accountable through release and post-release support.
Build with
the team.I guide implementation, review important changes, and help contributors grow. I stay hands-on and develop solution proposals with the architecture team.
Connect the
whole system.My work crosses product interfaces, AI execution, and platform reliability, including deployment supervision and rollback readiness.
Oct 2020 – Nov 2024
A foundation
in research.
Ho Chi Minh University of Technology
Bachelor of Computer Science
My individual undergraduate thesis surveyed out-of-distribution detection techniques: how models recognize inputs outside their training distribution.
Explore the thesis- GPA
- 3.5 / 4
- Individual thesis
- 10 / 10
From inference
to interfaces.
Tools from my current product work and earlier AI engineering projects. Each has a place in the work.
- AI & agents
- Python
- PyTorch
- LangChain
- Agno
- RAG
- Inference & edge
- vLLM
- SGLang
- TensorRT-LLM
- Triton Inference Server
- ONNX
- NVIDIA Jetson
- YOLOv8
- TensorRT
- Voice & real time
- Whisper
- WebSockets
- VAD
- STT / TTS
- FAISS
- Backend & data
- FastAPI
- NestJS
- PostgreSQL
- Redis
- DynamoDB
- Event-driven systems
- App & web
- Flutter
- Dart
- React
- Next.js
- TypeScript
- Cloud & delivery
- AWS
- GCP
- Cloud Run
- Cloud Build
- GKE
- Docker
- GitHub Actions
- CloudFormation
- Observability
- OpenTelemetry
- Sentry
- Grafana
- Prometheus
- Loki
- AWS X-Ray