Summary
Enterprise AI architect with deep expertise in generative AI systems, cloud-native architecture, and the full ML model lifecycle. Recent work includes RAG/GraphRAG architectures with vector databases, multi-agent AI systems using GCP ADK with Agent-to-Agent patterns, and LLM-powered security analysis platforms. Skilled at defining integration patterns across LLMs, vector databases, APIs, microservices, and event-driven workflows. Technical leadership spans architecture governance, cross-functional design reviews, and mentoring engineering teams. Committed to responsible AI, data governance, and security in production-ready solutions.
Technical Skills
- Languages
- Python, Java, TypeScript, JavaScript
- Cloud
- Google Cloud Platform, Microsoft Azure, Amazon Web Services
- GenAI / LLMs
- Claude (Opus/Sonnet), Claude Code, Gemini 3.0, ChatGPT, Llama, Prompt Engineering
- Agents / RAG
- LangChain, LangGraph, GCP ADK (A2A), RAG/GraphRAG, ChromaDB, Neo4j, FAISS
- ML / MLOps
- PyTorch, Vertex AI, MLflow, LLMOps
- Frameworks
- Spring Boot, React, Node.js, FastAPI, REST APIs, Microservices, IBM API Connect
- DevOps
- GitHub Actions, Jenkins, AWS CodePipeline, Docker, Kubernetes/OpenShift, Terraform
- Databases
- DuckDB, Oracle, PostgreSQL, MongoDB, Redis, SQLite
Experience
The Hartford Insurance Group, Hartford, CT
ADK-GitHubScanner – AI-Powered Security Analysis Platform
- Architected end-to-end multi-agent AI system using Google ADK with Gemini 3.0 for enterprise security vulnerability detection
- Designed Agent-to-Agent (A2A) orchestration with 3 specialized agents running in parallel via asyncio
- Implemented pure LLM-based security analysis replacing traditional SAST tools, achieving deterministic results (σ<5 score variance)
- Architected model-serving infrastructure optimized for latency and throughput using Vertex AI
- Designed cloud-native deployment on GCP Cloud Run with GCS storage and auto-scaling
- Established architectural standards including MAX_CAP deep analysis protocol with hallucination detection (<5% threshold)
- Deployed infrastructure using Terraform for GCP Cloud Run and associated cloud resources
- Implemented CI/CD pipelines using AWS CodePipeline for automated testing and deployment
- Mentored junior developers on AI best practices, prompt engineering, and responsible AI
CVSHealth / Aetna, Inc., Hartford, CT
Retrieval-Augmented Generation (RAG/GraphRAG) Architecture
- Led end-to-end architecture design for RAG/GraphRAG systems serving enterprise Data Science teams
- Architected integration patterns connecting LLMs to vector databases (ChromaDB) and graph databases (Neo4j)
- Implemented scalable RAG pipelines using Python, LangChain, LangGraph, and Vertex AI
- Designed containerized solutions deployed to GCP Cloud Run via GitHub Actions CI/CD
- Created A/B testing dashboard architecture to measure GraphRAG performance metrics
Enterprise Framework Modernization – AI-Assisted Architecture
- Led cross-functional initiative to modernize legacy Spring applications to Spring Boot
- Designed automated refactoring patterns using OpenRewrite and Claude Code for enterprise-scale migration
- Created reusable recipes and architectural standards adopted across multiple departments
- Facilitated architecture reviews and documented decision records for governance
Cloud-Native & Container Architecture
- Architected Platform-as-a-Service solution using Docker and Kubernetes/OpenShift
- Designed cloud ML platform patterns for Spring Boot applications in containerized environments
- Established CI/CD architectural standards using Jenkins, Nexus, and UDeploy
- Developed REST API architectures with Redis caching for high-throughput chat services
API & Microservices Architecture
- Led architecture governance for API management using CA Layer7 and IBM API Connect with OAuth2
- Designed integration patterns for mobile (iOS/Android) and web applications
- Implemented enterprise SSO architecture with Siteminder
- Created monitoring and observability dashboards using Splunk
Aetna Enterprise Framework (AEFW)
- Developed and maintained enterprise-wide Spring Framework used across the organization
- Created reference architectures and KickStart applications for rapid project onboarding
- Provided technical mentorship and support to project teams on architecture and deployment
Pratt & Whitney, UTC, East Hartford, CT
Advance Diagnostics Engine Management System (Phoenix)
- Designed and implemented enterprise system for airplane fleet engine management
- Built multiple web applications for diagnostics and fleet engine forecasting
- Evolved a robust Services Oriented Architecture using Spring, Tapestry, and Hibernate
Flight Acquisition System Transmission (FAST)
- Developed system using XML, Digital Signature/Encryption
- Created BIRT reports accessing Oracle database; built rapid reporting via JSP/Hibernate
Aetna, Inc., Hartford, CT
Enterprise Architecture Integration (EAI)
- Built middleware infrastructure for EAI components using MQSeries
- Supervised offshore and junior developers in EAI solutions and framework
Enterprise Portal
- Designed and implemented portal system following Sun J2EE best practices
Education & Certifications
M.S. Computer Information Systems
Boston University
2007
B.S. Information Systems
University of Connecticut
1998
Sun Certified Java Programmer
Java 2 Platform
2000
Personal Projects
MISO Energy Trading Platform
- Architected end-to-end multi-agent AI system for MISO energy market analysis with 541+ Python files across 6 core projects
- Designed DAG-based workflow orchestration with LLM routing supporting multiple providers (Anthropic Claude, Ollama)
- Implemented PyTorch LSTM-based ML models for congestion forecasting with MLflow experiment tracking
- Built hybrid ML training infrastructure: Mac Mini M4 with MPS GPU acceleration and Dell Xeon server for large batch training
- Built graph analysis system using DuckDB with Property Graph Query extension (2,546 vertices, 623K edges)
- Architected MCP server for Claude Code integration enabling natural language database queries
- Developed production FastAPI web dashboard with Plotly.js visualizations and multi-ISO support
- Achieved 70x optimization on async data collection pipelines (38 seconds per day)