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

Rafael Lopes, a production AI engineer in Vancouver, BC, builds and ships hybrid-RAG pipelines, distributed LLM inference across four compute architectures, and a sovereign research copilot on a self-hosted homelab, documenting everything in the open. His platform serves live traffic from a K3s cluster with no cloud compute, using GitOps and Cloudflare Tunnel for edge security.

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 .