ABOUT / THE ENGINEER

Backend foundations.
Applied AI systems.

I build the infrastructure and application layer around modern AI—agents, retrieval, evaluation, APIs, data models, and the operational systems that make them dependable.

LET'S TALK →VIEW RESUME ⇩
5+YEARS EXPERIENCE
BACKENDENGINEERING FOUNDATION
APPLIED AICURRENT DIRECTION
BUILDINGIN PUBLIC

PROFILE / 01

The direction

I approach AI as a backend engineer: models are powerful components, but the product succeeds or fails on the system around them.

My work sits where software architecture meets applied AI—designing reliable flows around uncertain model behavior, keeping data and interfaces explicit, and measuring whether a system is actually useful.

FOUNDATIONBackend Engineering

APIs, data, reliability, distributed systems

DIRECTIONApplied AI Engineering

Agents, RAG, evaluation, AI infrastructure

NOW / 02

Current focus

  • 01Agentic systems
  • 02RAG and retrieval
  • 03AI evaluation
  • 04Backend architecture
  • 05PostgreSQL
  • 06Developer tooling

METHOD / 03

How I work

01

Systems before demos

The useful part of AI starts after the model call: data, interfaces, failure handling, evaluation, and operations.

02

Evidence over claims

Projects, experiments, code, and measured results should make engineering ability visible without exaggerated language.

03

Clarity is engineering

Good architecture and good explanation share the same discipline: explicit boundaries, useful names, and honest tradeoffs.

TOOLS / 04

Working stack

LANGUAGES
TypeScriptJavaPythonGo
BACKEND
Node.jsNestJSFastAPIREST
DATA & AI
PostgreSQLRedisRAGAgents
OPERATIONS
DockerCI/CDObservability

CONTACT / 05

Have an interesting system to build?

I'm open to thoughtful engineering work around backend platforms and applied AI.

START A CONVERSATION →