About
I'm a hands-on AI Platform Engineer. I design the architecture and build the systems: Kubernetes manifests, FastAPI/Go services, CI/CD pipelines, evaluation harnesses, and Grafana dashboards. Not just slides and diagrams. I write the code and deploy the infrastructure.
I build private, production-grade AI systems for regulated environments. Your data stays in your VPC. Compliance requirements get met. And the platform actually works at scale.
Currently: Full-stack architect at Wilxite. Building expertise in private AI platforms. Also built FileCurator for creative professionals. Open to consulting engagements and the right senior/staff role.
What I Do
I work with organizations that can't use public cloud APIs. Maybe it's regulatory constraints (GDPR, HIPAA, FCA). Maybe it's data sensitivity. Or maybe it's cost at scale. My work covers three areas:
- Document Intelligence: OCR, layout extraction, and structured data pipelines for invoices, contracts, and forms
- Private LLM Platforms: Self-hosted inference with GPU scheduling, eval-driven releases, and guardrails
- Production RAG: Retrieval systems with chunking strategies, caching, and measured accuracy. Not just vector DB demos.
How I Work
Eval-Driven Releases
Every model update goes through a gold-set evaluation. If precision drops below your SLO, it doesn't deploy. No vibes, just metrics.
Kubernetes + GPU Orchestration
I deploy on Kubernetes with proper GPU scheduling (MIG when appropriate), autoscaling, and cost telemetry. Your infrastructure, your control.
Observability First
Grafana dashboards tracking precision/recall over time, p95 latency, cost per request, and drift detection. You can't improve what you don't measure.
Security & Compliance
Full audit logs, encryption at rest and in-transit, SBOM generation for vulnerability tracking, and no data leaving your environment.
Tech Stack
I choose tools based on your constraints, not the latest hype. That said, here's what I reach for most often:
Values
- Clarity over cleverness: Code should be readable in 6 months. KISS beats DRY beats clever.
- Measure, don't guess: If you can't measure accuracy, you can't improve it. Gut feelings don't scale.
- Production-grade or nothing: Demos are easy. Production is hard. I focus on the latter.
- Your data, your control: No vendor lock-in. You own the deployment, the data, and the roadmap.
Let's Talk
Building private AI systems and need someone who's done it before? Or have an exceptional senior/staff platform engineering role? Let's have a conversation.