I build agentic AI systems at scale that solve real enterprise problems through intelligent automation and orchestration.

Most of my work focuses on production-grade implementations: architecting multi-agent systems, building RAG pipelines that actually work, and designing patterns that scale from prototype to production on AWS infrastructure.

I help organizations move beyond proof-of-concepts to deploy AI agents that are reliable, observable, and cost-effective in real business environments.

What I work on

• AI Agent Architectures: multi-agent orchestration, agentic workflows, autonomous decision systems
• Production RAG: semantic search with OpenSearch, hybrid retrieval patterns, context optimization
• AWS AI Infrastructure: Amazon Bedrock integrations, serverless agent deployments, scaling patterns
• Agent Reliability: evaluation frameworks, observability patterns, cost optimization strategies
• LangChain Production Patterns: moving from notebooks to production-ready agent systems
• Developer Workflows: how to structure agent code, handle tools, manage state, and build testable agents

• Principal Solutions Architect at AWS helping enterprises design, build, and scale production AI agent systems using Amazon Bedrock, OpenSearch, and cloud-native architectures.

• Expert in building agentic AI systems using Strands (AWS's internal agentic framework), LangChain, and Python for large-scale deployments.

• Focus on bridging the gap between AI research and enterprise production requirements—making AI agents that actually ship and perform reliably at scale.

🌱 Building production AI agents or scaling agentic systems? Let's connect. Always happy to discuss architecture patterns, scaling challenges, and what actually works in production.