Transforming 27 years of building mission-critical systems experience into creating production-ready AI solutions that actually scale
After architecting healthcare systems that process millions of transactions, I'm now focused on building AI systems. I specialize in the crucial 90% of work that happens after the model is trained—authentication, compliance, scalability, and reliability.
- Production RAG Systems: Multi-database architectures handling 1000+ concurrent users
- Compliant AI: HIPAA-eligible AI agents with built-in safety guardrails
- Scalable Infrastructure: Zero-downtime deployments with blue-green workflows
- Open Source Tools: Contributing to the gap between AI research and production
- RAG & Agents: LangChain, LangGraph, LlamaIndex
- Vector Search: MongoDB Atlas, Pinecone, pgvector, FAISS
- ML/Ops: MLflow, Dagster, Prefect, Ray Serve
- NoSQL: MongoDB Atlas, ScyllaDB, DynamoDB
- SQL: PostgreSQL, MySQL, RDS
- Cache & Queue: Redis, RabbitMQ, Kafka
- Cloud: AWS (Solutions Architect Pro), Azure, GCP
- IaC: Terraform, CloudFormation, AWS CDK
- Orchestration: Kubernetes, ECS, Docker Swarm
- Healthcare: HIPAA, HL7 FHIR, CAQH CORE
- Security: Zero-Trust, OAuth 2.0, JWT, WAF
- Governance: SOC2, GDPR, Audit Logging
Production-grade RAG system with enterprise features
- 🏗️ 4-database architecture optimized for different workloads
- 🔐 Built-in authentication, rate limiting, and billing
- 📈 Handles 1000+ concurrent users with <100ms latency
- ✅ Comprehensive test coverage and CI/CD pipeline
Battle-tested Terraform modules for AI workloads
- 🌐 Multi-AZ deployment with automatic failover
- 🔒 HIPAA-eligible security configurations
- 💰 Cost-optimized with automated scaling
- 📊 Built-in monitoring and alerting
- 🏆 AWS Certified Solutions Architect – Professional
- 🤖 AWS Certified Machine Learning – Specialty
- 🏛️ TOGAF Certified Enterprise Architect
- 🔬 Databricks Certified Machine Learning
- 🏥 HL7 FHIR Proficiency
current_project = {
"name": "Emotional AI Companion",
"phase": "Building production-ready infrastructure",
"stack": ["LangGraph", "Ray Serve", "PostgreSQL", "Terraform"],
"goal": "HIPAA-compliant conversational AI with durable state"
}
interests = [
"Bridging the gap between AI demos and production",
"Building compliant AI systems for regulated industries",
"Scaling stateful AI agents to thousands of users",
"Open source tools for production AI"
]"The difference between a demo and production isn't the AI model—it's the 90% of 'boring' stuff that makes it reliable, secure, and scalable."