Building intelligent systems that transform enterprise operations
Enterprise AI Architect with deep expertise in generative AI systems, cloud-native architecture, and the full ML model lifecycle. Recent accomplishments include designing RAG/GraphRAG architectures with vector databases (ChromaDB, Neo4j, FAISS), deploying multi-agent AI systems using GCP ADK with Agent-to-Agent (A2A) patterns, and architecting LLM-powered security analysis platforms with Gemini 3.0.
Skilled at defining integration patterns across LLMs, vector databases, APIs, microservices, and event-driven workflows. Technical leadership experience spans architecture governance, cross-functional design reviews, and mentoring engineering teams on AI best practices. Committed to embedding responsible AI, data governance, and security requirements into scalable, production-ready solutions.
A selection of enterprise AI and cloud architecture work
Multi-agent AI system for energy market analysis featuring DAG-based workflow orchestration, PyTorch LSTM-based ML models with MLflow tracking, and graph analysis using DuckDB with Property Graph Query extensions.
Medical Billing System with React frontend (CloudFront), Python API server (Lambda), and SQLite DB. Audit tab uses Claude (Sonnet 4) and other LLMs to audit claims.
Medical Billing System with React frontend, Python API server, and Postgres DB. Audit tab uses Ollama LLM to audit claims.
Retrieval Augmented Generation systems running on Google Cloud Run with multiple deployment configurations.
Retrieval Augmented Generation systems for enterprise knowledge management, combining vector databases with LLM capabilities including resume RAG and ASOP RAG implementations.
Context and Cache Augmented Generation systems for enterprise knowledge management with different embedding model implementations.
Professional resume detailing 20+ years of experience in enterprise architecture and technical leadership.
Open Web UI installed on Dell Xeon 64 GB RAM and Mac Mini M4 16 GB to compare response time for Ollama models.
Custom-built enterprise chatbot leveraging Ollama's open-source AI models for natural language processing and contextual responses.
This site is an example of continuous integration and continuous delivery (CI/CD) with automated SSH deployment.
Model Context Protocol system with MCP Java SDK implementation for time retrieval and other integrations.
Distributed setup combining high-memory batch processing with GPU-accelerated inference
Data pipelines, ETL processing, and large batch ML training
ML inference and model serving - 3-4x faster than CPU-only training
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