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Founding Engineer scaling AI from prototype to production. I architect agentic RAG systems, predictive ML pipelines, and compliance automation that run in enterprise environments.
class AIEngineer:
def __init__(self):
self.focus = [
"Agentic RAG & Multi-Agent Orchestration",
"Predictive Maintenance & Anomaly Detection",
"Regulatory Compliance Automation (NERC CIP)",
"Cloud-Native ML (Azure AI Foundry, GCP Vertex AI)"
]
def deploy(self, agent) -> Production:
return agent.scale_to_enterprise()| Business | Description | Action |
|---|---|---|
| Spec Agents | AI-powered RAG for engineering teams—instant access to ASTM standards and steel specifications with cited answers in <2s |
| Predictive Agent | Compliance Agent | Anomaly Agent |
|---|---|---|
| LSTM-Based RUL Prediction | NERC CIP Automation | Real-Time Monitoring |
| Extending turbine life 15-20% using neural networks trained on NASA C-MAPSS and GE 7FA turbine patterns | Reducing audit prep 60% through automated procedure validation against CIP-006-6 requirements | Auto root cause analysis using Isolation Forest for vibration, temperature, and pressure data |
experiments/
├── enterprise-rag/ # Agentic RAG for industrial documents
├── vision-ai-safety/ # VLM for HSE compliance (Qwen2-VL)
├── data-parser-energy/ # Well log ETL (10x faster)
└── rl-supply-chain/ # PPO inventory optimization (-25% stockouts)
| Experiment | Stack | Code |
|---|---|---|
| Enterprise RAG | LangGraph • Pinecone • FastAPI | |
| Vision AI Safety | Qwen2-VL • Transformers • Gradio | |
| Data Parser Energy | LASIO • Pandas • NumPy | |
| RL Supply Chain | Stable-Baselines3 • PPO • Gymnasium |
+ LangGraph → Refactored FunctionMessage patterns, Enhanced fine-tuning docs
+ AutoGen → Fixed Azure AI Client streaming stability
+ CrewAI → URL validation for Azure Gateways
+ Transformers → Documentation improvements
┌────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ AI/ML │
│ ├── LLMs: OpenAI, Claude, Gemini, Mistral │
│ ├── Agents: LangGraph, AutoGen, CrewAI, PydanticAI │
│ ├── Vector DBs: Pinecone, ChromaDB, FAISS, Azure AI Search │
│ └── MLOps: MLflow, W&B, Model Monitoring │
├────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ Infrastructure │
│ ├── Cloud: Azure AI Foundry, GCP Vertex AI, AWS SageMaker │
│ ├── Containers: Docker, Kubernetes (AKS/GKE) │
│ └── IaC: Terraform, GitHub Actions │
├────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ Domain │
│ ├── Power: CCGT, Gas Turbines, SCADA/Historian │
│ ├── Grid: ERCOT, ISO Markets, Dispatch Optimization │
│ └── Regulatory: NERC CIP, EPA Emissions, Safety Compliance │
└────────────────────────────────────────────────────────────────────────────────────────────────────┘
| M.S. Artificial Intelligence | Experience |
|---|---|
| University of Colorado Boulder (2027) | 5+ years production software • 4+ years AI systems |
Started as a founding engineer scaling a startup from zero to production. Now I build AI that operators trust with million-dollar equipment decisions.