Full-Stack Engineer specialized in building AI-powered applications and high-performance web platforms that turn complex workflows into simple, automated solutions.

Problem Solved

The challenge was creating a modern, accessible website for a local cleaning business that could handle service listings, client testimonials, and booking inquiries while maintaining fast load times and mobile responsiveness. The solution needed to improve online visibility and convert visitors into customers.

Key Features

• Service listings with detailed descriptions and pricing
• Interactive booking forms with validation
• Client testimonials section
• SEO-optimized content structure
• Accessibility improvements (WCAG 2.1 compliant)
• Responsive design for all device sizes

Quantifiable Results

Boosted client inquiries by 20% through improved user experience and SEO optimization. Achieved 95+ Lighthouse performance score and improved accessibility score to 100, resulting in better search engine rankings and user engagement.

Technology Stack

Infrastructure & DevOps

Deployed on Vercel's edge network for optimal global performance. Utilized Next.js Server Components for improved initial load times and SEO. Implemented automated deployments via Git integration.

System Architecture

Code Highlight

// Next.js Server Component for Service Listing
export default async function ServiceList() {
  const services = await getServices();
  
  return (
    <div className="grid gap-6">
      {services.map(service => (
        <ServiceCard key={service.id} {...service} />
      ))}
    </div>
  );
}

Further Reading

View full documentation and technical decisions on GitHub →

Problem Solved

The challenge was building a microservice architecture capable of reliable, low-latency scaling during peak sales events, requiring container orchestration. The platform needed to handle real-time inventory updates, secure payment processing, and provide analytics dashboards for administrators.

Key Features

• Real-time inventory tracking with WebSocket connections
• Secure payment gateway integration
• Admin analytics dashboards with data visualization
• Modular component library for rapid feature development
• Containerized deployment with Docker
• CI/CD pipeline with automated testing

Quantifiable Results

Achieved 99.8% uptime using containerized services. Reduced API response latency by 15% by leveraging CloudFront caching. Implemented automated scaling that handles 10x traffic spikes during sales events without manual intervention.

Technology Stack

Infrastructure & DevOps

AWS (EC2, S3, RDS, CloudFront), Docker (Containerization), GitHub Actions (CI/CD Pipeline). The application uses containerized services deployed on AWS EC2 instances, with static assets served through CloudFront CDN for optimal global performance. Database hosted on AWS RDS with automated backups.

System Architecture

Code Highlight

# Dockerfile for Node.js API Service
FROM node:18-alpine
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .
EXPOSE 3000
CMD ["node", "server.js"]

Further Reading

Why I chose PostgreSQL over MongoDB for inventory management →

Problem Solved

Organizations struggle with extracting meaningful insights from large volumes of documents. Traditional document management systems lack intelligent search capabilities, and manual analysis is time-consuming and error-prone. DocuMind AI solves this by combining advanced RAG technology with enterprise-grade security, enabling users to upload documents, automatically index them using vector embeddings, and query them using natural language with AI-powered responses.

Key Features

• Secure document upload and cloud storage (Cloudflare R2, AWS S3, MinIO support)
• Advanced RAG pipeline with document ingestion, chunking, embedding, and vector storage
• Hybrid search combining semantic vector search with keyword-based retrieval
• Multi-LLM support (OpenAI, Google Gemini, Anthropic Claude, Ollama, HuggingFace)
• Multi-vector database support (ChromaDB, Pinecone, Qdrant)
• Real-time document analysis with split-screen interface
• Project-based document organization with tagging and metadata
• Query history and conversation management
• Enterprise authentication and authorization
• Production-ready FastAPI backend with comprehensive API

Quantifiable Results

Currently in active development (~40% complete). The backend architecture is 100% complete with FastAPI, middleware, error handling, and logging. The RAG pipeline is fully implemented with document ingestion, chunking, embedding, vector storage, retrieval, and generation. MongoDB database and cloud storage abstraction layer are complete. Frontend UI is ~70% complete with comprehensive components. Next steps include completing frontend API integration and adding enterprise features (SSO, 2FA, audit logs).

Technology Stack

Infrastructure & DevOps

Built with a three-tier, asynchronous architecture with clear separation of concerns. The backend uses FastAPI with comprehensive middleware for security, CORS, rate limiting, and error handling. Document processing is handled asynchronously with background task queues. Storage abstraction layer supports multiple backends (currently configured with Cloudflare R2). The application is containerized with Docker and ready for cloud deployment.

System Architecture

Code Highlight

# FastAPI RAG Query Endpoint
@router.post("/query")
async def query_documents(
    project_id: str,
    query: str,
    llm_provider: str = "openai"
):
    # Retrieve relevant document chunks
    chunks = await vector_store.similarity_search(
        query=query,
        project_id=project_id,
        top_k=5
    )
    
    # Generate AI response using retrieved context
    response = await llm_service.generate(
        query=query,
        context=chunks,
        provider=llm_provider
    )
    
    return {"answer": response, "sources": chunks}

Further Reading

View full documentation, implementation status, and technical decisions on GitHub →