I build intelligent products from model to production.

I'm a Senior Full-Stack AI Engineer focused on turning AI capabilities into reliable, scalable software.

My work sits at the intersection of AI engineering, backend architecture, frontend systems, and cloud infrastructure — from designing agentic workflows and RAG systems to shipping polished products that real users can actually use.

I care about more than making an AI demo work.

I care about making it fast, reliable, observable, secure, maintainable, and production-ready.

                    ┌──────────────────────────┐
                    │      AI APPLICATIONS     │
                    └────────────┬─────────────┘
                                 │
             ┌───────────────────┼───────────────────┐
             ▼                   ▼                   ▼
       🤖 AI Agents          🔎 RAG Systems      🧠 LLM Apps
             │                   │                   │
             └───────────────────┬───────────────────┘
                                 ▼
                    ⚙️ Production Backend
                                 │
             ┌───────────────────┼───────────────────┐
             ▼                   ▼                   ▼
          APIs / DB           Cloud / DevOps      Observability
             │                   │                   │
             └───────────────────┬───────────────────┘
                                 ▼
                    🚀 Real User Experience

• 🤖 LLM Applications & AI Agents
• 🧠 RAG & Knowledge Systems
• 🔄 Multi-Agent Architectures
• 🛠️ AI-powered Developer & Business Tools
• ⚡ High-performance Backend Systems
• 🌐 Full-Stack Product Engineering
• ☁️ Cloud Architecture & Deployment
• 🔐 Production Security & Reliability
• 📊 Evaluation, Observability & AI Quality

Research automation powered by multiple AI agents.

A system designed to break complex research tasks into specialized workflows, coordinate agents, gather information, and synthesize useful results.

Focus: AI Agents · LLMs · Research Automation · Python

AI-powered document understanding and extraction.

Exploring practical ways to transform unstructured documents into structured, searchable, machine-readable information.

Focus: Python · Document AI · LLMs · Information Extraction

AI-driven growth and business intelligence system.

Built as part of the CockroachDB × AWS Hackathon 2026, combining application engineering with AI-driven workflows and modern cloud infrastructure.

Focus: Python · AI · AWS · CockroachDB

A full-stack application focused on building a polished customer-facing experience with modern web technologies.

Focus: JavaScript · Full-Stack Development · Product Engineering

An experimental AI-powered application exploring intelligent interactions and modern TypeScript application architecture.

Focus: TypeScript · AI · Application Architecture

A cross-platform workspace application built with Flutter/Dart.

Focus: Dart · Flutter · Cross-Platform Development

LLMs · AI Agents · RAG · Embeddings · Vector Search · Prompt Engineering · AI Evaluation · Multi-Agent Systems

Python · FastAPI · Node.js · REST APIs · Async Systems · Microservices

TypeScript · JavaScript · React · Next.js · Modern Web Architecture

PostgreSQL · CockroachDB · Redis · Vector Databases · SQL

AWS · Docker · CI/CD · GitHub Actions · Cloud Architecture · Observability

System Design · API Design · Distributed Systems · Testing · Security · Performance

I don't build AI features just because they're possible.

I focus on useful systems with measurable outcomes.

A successful AI application needs more than a good model.

It needs:

Reliability → Evaluation → Observability → Security → Scalability → UX

The LLM is only one part of the system.

The real engineering challenge is designing the architecture around it.

User
 │
 ▼
Product / UX
 │
 ▼
Application Layer
 │
 ├── AI Agents
 ├── RAG
 ├── Tools
 ├── Memory
 └── Workflows
 │
 ▼
Model Layer
 │
 ▼
Data + Infrastructure
 │
 ▼
Observability / Evaluation

I learn best by taking difficult ideas and turning them into working systems.

That means experimenting, breaking things, measuring results, and shipping.

AI Agents
    ↓
Agentic Workflows
    ↓
Long-term Memory
    ↓
Tool Use
    ↓
Multi-Agent Systems
    ↓
Reliable AI Infrastructure

I'm particularly interested in the next generation of AI-native software where intelligent systems become deeply integrated into the way applications are designed and operated.

Thanks for stopping by.