Production AI Platform Design and Deployment
Designed and deployed a production AI infrastructure platform: GPU cluster networking, model serving via vLLM, RAG pipeline with pgvector, and observability stack — processing real data daily.
The problem
The organization needed a production AI platform for internal tooling and customer-facing features but had no existing ML infrastructure. Requirements included multi-model serving, RAG over proprietary documents, and integration with existing network security controls. The team needed someone who understood both the AI/ML side (GPU scheduling, model serving, vector databases) and the infrastructure side (networking, security, monitoring) to design something that would actually work in production.
The approach
We designed and deployed a complete AI infrastructure stack: DGX Spark and consumer GPU (3090) cluster for model serving via vLLM and LiteLLM, PostgreSQL with pgvector for RAG embeddings, n8n for workflow orchestration (20+ automated workflows including email triage, meeting intelligence, and network drift detection), Next.js dashboard for the application layer, and Prometheus/Grafana for observability. The platform was integrated with existing security controls (CrowdSec, Traefik reverse proxy) and designed for maintainability — automated config management, drift detection, and compliance checks for the AI workloads alongside traditional infrastructure.
Results
- Production AI platform serving multiple models with sub-second inference
- RAG pipeline with measurable retrieval accuracy over proprietary document corpus
- 20+ automated AI workflows processing real data daily
- Unified observability across AI and traditional infrastructure
- Platform designed for single-person maintenance with automated drift detection
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