# AI Engineer in Vancouver, BC — Production AI, Built in the Open

> Source: <https://blog.r-lopes.com/posts/ai-engineer-vancouver>
> Published: 2026-06-05 14:00:00+00:00

## What I Build

I'm Rafael Lopes — "Rafa" — a production AI engineer based in Vancouver, British Columbia. I don't write *about* AI from the sidelines; I ship it. The systems below all serve live traffic from a self-hosted cluster in one room:

- A
**hybrid-RAG pipeline** over 69,000+ curated technical chunks (BM25 + TF-IDF + weighted RRF + cross-encoder rerank), with an automated quality gate that strips fabricated quotes before anything publishes. **Distributed LLM inference** across four compute architectures — ARM, AMD ROCm, NVIDIA CUDA, and Apple Silicon — pooling memory over the llama.cpp RPC protocol for models too large for one GPU., a sovereign research copilot for Canadian HPC — every byte of the inference path stays local, with a live ledger proving zero foreign hops per query.[exaflop.ca](http://exaflop.ca)

## The Stack

The whole platform is documented, not described:

**How the briefs are made**→ the retrieval → synthesis → quality-gate → publish pipeline, with the real numbers.** The infrastructure**→ a four-architecture K3s homelab, GitOps via Argo CD, Cloudflare Tunnel + Zero Trust at the edge — no cloud compute.** A from-scratch RAG build**→ the actual BM25/TF-IDF/RRF code and measured retrieval quality.

## The Daily Brief

Every weekday I publish a cross-domain engineering brief — AI, web performance, system design, security, and the career arc — synthesized from the corpus, cited to source, and shipped through the same quality gate. The archive is the proof of consistency: nobody fakes a dated, cited, cross-domain brief every working day.

## The Infrastructure

No managed Kubernetes, no hosted CI, no hyperscaler in the data path. A Raspberry Pi runs the K3s control plane; an AMD-ROCm workstation does the GPU heavy lifting; an x86 box self-hosts GitLab and the registry; a Mac M3 Max joins as an RPC peer. Every change goes git → CI → Argo CD → live. The platform that runs this blog is the same one that runs the research copilot.

## Available For

Vancouver-based and remote-friendly. Open to:

**Consulting** on production RAG, LLM inference, and AI platform/SRE work.**Speaking** on sovereign/local-first AI, web performance for AI consumers, and homelab-scale inference.**Collaboration** with teams shipping real AI infrastructure who want the receipts, not the hype.

Teaching by doing — production AI, not commentary. The system is the proof.

## FAQ

**Who is the AI engineer in Vancouver behind this site?**
Rafael Lopes ("Rafa") — a production AI engineer based in Vancouver, British Columbia. He builds and ships RAG pipelines, distributed LLM inference, and a sovereign research copilot on a self-hosted homelab, and documents the results in the open.

**What does a production AI engineer do?**
Builds AI systems that serve real traffic — retrieval pipelines, LLM inference, quality gates, and the platform/SRE work to run them — rather than writing about AI from the sidelines. Here, every claim links to a live system or a measured number.

**What AI does Rafael Lopes build?**
Hybrid retrieval (BM25 + TF-IDF + weighted RRF + cross-encoder rerank), distributed LLM inference across four compute architectures over the llama.cpp RPC protocol, and [exaflop.ca](http://exaflop.ca) — a sovereign, local-first research copilot for Canadian HPC.

**Where can I read more?**
The daily cross-domain engineering brief, the how-it-works pipeline, and the infrastructure write-up — all linked below and at [blog.r-lopes.com](https://blog.r-lopes.com).