Keywords: AI Architect Healthcare AI Production MLOps LLM Deployment RAG Systems Enterprise Architecture HIPAA Compliance Therapeutic AI Multi-Agent Systems AI Governance
Atlanta, GA | Open to Principal/Staff AI Architect Opportunities
I turn AI demos into production systems that pass regulatory audits, scale to millions, and deliver measurable business outcomes.
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Production-Grade Multi-Agent Therapeutic AI | 15 Microservices | 100% Crisis Detection | HIPAA Compliant
A complete AI system I designed and built from scratch for elderly mental health care:
| Metric | Achievement |
|---|---|
| Crisis Detection | 100% recall, <1s response (30x faster than regulatory) |
| Edge Architecture | 90% on-device / 10% cloud processing |
| Training Data | 2,871 crisis patterns + 33,047 therapeutic patterns |
| FDA Pathway | De Novo Q2-Q3 2027 (60% probability) |
| Intent Classification | 214 prototypes across 10 therapeutic categories |
| Market Opportunity | $1.3B-2.0B TAM (US/EU/UK, 2.5M+ residents) |
6-Layer Architecture:
- Client Layer: 6 healthcare dashboards + WebSocket + Voice + REST API
- AI Services: AI Router, BGE Embeddings (768-dim), Whisper STT, Piper TTS
- Therapeutic Agents: 7 specialized agents (Conversational, Reminiscence, Behavioral, Grounding, Safety, Social, Web Search)
- Safety Layer: Crisis Detector V4, Ensemble Classifier, Trajectory Analyzer, Crisis Explainer (XAI)
- RAG System: Hybrid retrieval (BM25 + Semantic + RRF + Cross-encoder re-ranking)
- Data Layer: PostgreSQL + pgvector, Redis 4-layer cache, Langfuse monitoring
Strategic Differentiators:
- Edge-First: 90% on-device processing, 72-hour offline capability
- Affective AI: Scherer's CPM emotion model, +11.76% engagement improvement
- Entity-First NLU: Context-aware routing for deceased relatives, medical conditions
- EU AI Act Ready: High-risk classification compliance, Article 5(1)(f) exemption
ML Models & Clinical Evidence:
- Qwen 2.5-7B-Instruct on Apple Silicon (Metal GPU, llama.cpp inference)
- Evidence-based outcomes: 35% depression reduction, 40-60% anxiety reduction
- 4-level crisis stratification: IMMEDIATE (<30s) → URGENT → ELEVATED → MODERATE
The Definitive Enterprise AI Deployment Guide | 480+ Checklist Items | 20 Domains | CRISP-DM Aligned
Addressing why 87% of ML projects fail to reach deployment:
| Metric | Value |
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| Checklist Items | 480+ production-ready checks |
| Domains | 20 (Architecture, Data Quality, Agentic AI, Security, Red Teaming, FinOps, Governance, Healthcare AI, and more) |
| Lifecycle Model | CRISP-DM aligned 8-stage framework with gate requirements |
| Industry Frameworks | Gartner, OWASP LLM Top 10, NIST AI RMF, EU AI Act, ISO 42001 |
| Companion Guides | 7 deep-dive docs (MLOps Maturity, Lifecycle Stages, Failure Taxonomy, Case Studies) |
Key Features:
- MLOps Maturity Model assessment (Level 0-3)
- Healthcare AI section with FDA regulatory overlay
- Assured Intelligence for safety-critical systems (conformal prediction, causal validation)
- Interactive HTML checklist with auto-scoring, CSV template, C4 architecture diagrams
"After 27 years of enterprise systems and analyzing $15B+ in AI failures (IBM Watson, Zillow, Babylon Health), I compiled everything you need to avoid the mistakes that killed billion-dollar AI projects."
RAG Pipeline | Vector Search | LLM Integration | Multi-Database Architecture
Production-ready RAG demonstrating polyglot persistence and enterprise patterns:
| Component | Implementation |
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| RAG Pipeline | LangChain orchestration with semantic search |
| Vector Store | MongoDB Atlas with embedding indexing |
| Polyglot Persistence | PostgreSQL (ACID), ScyllaDB (logs), Redis (cache) |
| LLM Support | OpenAI GPT + local Qwen fallback |
| Architecture | Two-plane design for independent scaling |
Enterprise Patterns: JWT/OAuth2 auth, billing service, rate limiting, structured logging, zero-downtime deployments
Production-Grade AWS VPC Module | Multi-AZ | Infrastructure as Code | Enterprise-Ready
A reusable Terraform module for deploying secure, scalable AWS VPC infrastructure—the foundation for production AI workloads:
| Metric | Value |
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| Resources Orchestrated | 15+ AWS resources |
| Network Capacity | 65,000+ IPs (/16 VPC) |
| Deployment Time | ~3 minutes |
| Availability | Multi-AZ (2-6 zones) |
- Network Architecture: Public/private subnet segmentation with NAT Gateway for secure egress
- Security Features: VPC Flow Logs, Network ACLs, private-first design pattern
- Enterprise Patterns: Consistent tagging, input validation, modular structure
- CI/CD Pipeline: GitHub Actions with automated Terraform validation
Production-Ready Python Project Template | Cookiecutter | Modern Tooling | Best Practices
A zero-config Cookiecutter template that scaffolds production-ready Python projects with modern tooling and automated quality gates:
| Metric | Value |
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| Linting Speed | 10-100x faster (Ruff vs Black+isort+Flake8) |
| Type Coverage | 100% with Mypy strict mode |
| Setup Time | ~30 seconds to scaffold |
| Quality Gates | 5 automated pre-commit hooks |
- Modern Toolchain: Ruff (unified linter/formatter), Mypy, Pytest, Bandit security scanning
- Automated Quality: Pre-commit hooks for lint, format, type-check, and security on every commit
- Production Patterns: Typer CLI scaffolding, Rich terminal UI, conventional commits with Commitizen
- Rich Documentation: Mermaid diagrams for architecture, workflow, and toolchain visualization
flowchart LR
CODE[Your Code] --> RUFF[Ruff] --> MYPY[Mypy] --> PYTEST[Pytest] --> BANDIT[Bandit] --> COMMIT[Commit]
"The foundation I use for all my AI/ML projects—consistent tooling means I can focus on solving problems, not configuring environments."
Languages & Frameworks
AI/ML & LLM Infrastructure
Databases & Storage
Infrastructure & DevOps
TOGAF 9.1 Certified Enterprise Architect |
HL7 FHIR Healthcare Interoperability |
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ITIL 4 Foundation |
In Progress
Google Cloud Professional Machine Learning Engineer |
Google Cloud Professional Cloud Architect |
| Milestone | Description | Status |
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| Lilo Engine Core | 15 microservices, 7 therapeutic agents, 100% crisis recall, 35K+ training patterns |  |
| Safety-Critical Infrastructure | IEC 61508 compliant safety kernel, formal verification |  |
| EHR Integration | Epic & Cerner via FHIR R4, SMART on FHIR OAuth 2.0 |  |
| Edge-Cloud Architecture | 90% on-device / 10% cloud, K3s + Jetson for <50ms local inference |  |
| FDA De Novo Submission | Clinical validation (n=100), Q2-Q3 2027 clearance target (60% probability) |  |
| International Expansion | EU (CE Marking MDR), UK (UKCA), 2.5M+ residents addressable |  |
| RPM Device Integration | Vitals monitoring with Medicare billing ($1,560/patient/year) | |
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Building the next evolution of the therapeutic AI platform with formal safety guarantees. Next Milestone: Safety Kernel + Device Abstraction Layer
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"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."
Looking for a Principal/Staff AI Architect who can deliver production AI systems?
27 years of progressive technical leadership | C-suite vision to hands-on delivery | Enterprise AI at scale