I am an AI Engineer with 3 years of production experience building LLM-powered systems that serve thousands of daily users. I specialize in RAG pipelines, multi-agent architectures, and scalable APIs.
I am a strong Python developer with hands-on experience in LangChain, FastAPI, and vector databases. I am eager to leverage my background in generative AI and system design to build impactful AI products.
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Multi-Agent Architecture: Powered by CrewAI, utilizing specialized agents for research, writing, and review.
RAG for Brand Alignment: Integrated with LlamaIndex to retrieve context from uploaded brand documents (PDF, DOCX, TXT).
Self-Learning Feedback Loop: Automatically adapts to brand style based on human approvals and feedback.
Production Resilience:
- Circuit Breakers: Protects against cascading failures in external APIs (Groq, Tavily).
- Rate Limiting: Integrated token-bucket limits for API protection.
- Retry Logic: Exponential backoff for database and API operations.
- Persistent Storage: All brand documents are stored in PostgreSQL for persistence across deployments.
- Real-time Metrics: Analyzes brand tone, sentence structure, and signature phrases.
- Framework: FastAPI (Python)
- AI/LLM: Groq (Llama-3), CrewAI, LlamaIndex
- Database: PostgreSQL (SQLAlchemy + psycopg2)
- Monitoring: Structlog (Structured Logging)
- Deployment: Railway
BrandGuard AI is a production-ready, multi-agent marketing content generation system designed to create brand-aligned content that improves over time through human feedback. It leverages RAG (Retrieval-Augmented Generation) to ground generation in your specific brand voice and a self-learning loop to adapt to your style.
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Link to project:
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