I build resilient backend systems and AI-native architectures that scale elegantly.
My Desk
Every tool is a deliberate choice. I optimize for flow state and deep work.
MacBook Pro M3 Max
Studio Display
HHKB Professional
Alacritty + Tmux
Dot Grid Leuchtturm
Pour Over Coffee
Engineering Philosophy
Beliefs forged through production outages, failed deployments, and midnight debugging sessions.
Build systems people can maintain
Design before coding
Optimize only after measuring
Simple scales better
Documentation is engineering
Fail loudly and early
Security is a default, not a feature
Own the deployment
Boring technology is good
Embrace deletion
How I Think
The lifecycle of an engineering problem.
Engineering Values
Maintainability
Scalability
Readability
Performance
Security
Reliability
Curiosity
A Day Inside My Workspace
Structure prevents burnout. This is how I organize my mental energy.
Morning Research
Reading whitepapers, reviewing pull requests, and catching up on arXiv releases.
Deep Planning
Writing architecture documents, drawing flowcharts, and defining API schemas before touching code.
Flow State Coding
Headphones on. Terminal open. Executing the morning's architecture plan with zero distractions.
Testing & CI/CD
Writing unit tests, fixing pipeline failures, and ensuring Docker containers build cleanly.
Evening Reflection
Reviewing the day's diffs, updating documentation, and writing down tomorrow's hardest problem.
Career Timeline
Senior AI Engineer
Leading the architecture and deployment of multi-agent LLM systems and custom RAG pipelines.
Backend Engineer
Scaled FastAPI microservices to handle 10k RPS. Migrated legacy monolith to Kubernetes.
Software Engineering Intern
Built internal admin dashboards and optimized SQL queries reducing latency by 40%.
B.S. Computer Science
Specialized in Distributed Systems and Machine Learning. Published paper on vector search optimization.
First Production App
Deployed a full-stack Django application that acquired 1,000 active users in the first month.
The Bookshelf
The foundational texts that shaped my engineering mindset.
Designing Data-Intensive Applications
Martin Kleppmann
Deep Learning
Ian Goodfellow
Clean Architecture
Robert C. Martin
Site Reliability Engineering
Learning Roadmap
Production Ready
- Python/FastAPI
- TypeScript/React
- Docker/K8s
- PostgreSQL
- Redis
- Vector DBs (Qdrant)
Currently Learning
- Rust (for performance)
- CUDA optimization
- Multi-Agent Orchestration
Future Goals
- WebAssembly
- Custom LLM Training
- Hardware-accelerated Inference
What I'm Building Next
The frontier of my engineering research.
Autonomous Engineering Agents
Building a swarm of LLM agents capable of reading JIRA tickets, writing code, and running their own test suites before opening PRs.
Real-time Voice Intelligence
Moving away from text-based chatbots to sub-200ms latency voice models (like GPT-4o native audio) for industrial control systems.
Core Dumps (Fun Facts)
Frequently Asked Questions
The front-end ecosystem is incredibly mature. The real bottlenecks in modern software—where companies win or lose—are in how efficiently they process data, scale their inference pipelines, and securely retrieve context. That's where the hardest problems are, and that's where I want to be.