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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",
"Regulatory Compliance Automation",
"Cloud-Native ML (Azure AI Foundry, GCP Vertex AI)"
]
def deploy(self, agent) -> Production:
return agent.scale_to_enterprise()|
AI-powered RAG system for querying steel specifications with traceable citations. Query NACE MR0175, ASTM, and API standards instantly with answers engineers can cite in compliance reports. Next.js 16 • React 19 • TypeScript • Supabase pgvector • Voyage AI • Groq • Vercel
|
Production-ready MLOps platform monitoring 10 compressor units across 4 Texas stations. PySpark ETL pipelines process 50k+ sensor readings through a Bronze/Silver/Gold medallion architecture into real-time fleet health dashboards. PySpark • Delta Lake • PostgreSQL • Streamlit • Plotly • Docker • Terraform
|
experiments/
├── predictive-agent/ # LSTM-based RUL prediction for turbines
├── compliance-agent/ # NERC CIP compliance automation
├── anomaly-agent/ # Real-time turbine anomaly detection
└── vision-agent/ # VLM for HSE compliance (Qwen2-VL)
| Experiment | Stack | Code | Demo |
|---|---|---|---|
| Predictive Agent | LSTM • Scikit-Learn • Plotly • Docker |  |
 |
| Compliance Agent | PydanticAI • DSPy • Mistral • FastAPI | |
|
| Anomaly Agent | Isolation Forest • Gradio • Time-Series | |
|
| Vision Agent | Qwen2-VL • Transformers • Gradio | |
|
+ LangGraph → Refactored FunctionMessage patterns, Enhanced fine-tuning docs
+ Pydantic → Core library contributions
+ 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 │
└────────────────────────────────────────────────────────────────────────────────────────────────────┘
| University of Colorado Boulder — Expected 2027 | 5+ years production software · 3+ years building 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.