Rafael LopesMachine-readable brief — Rafael Lopes
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Rafael Lopes · Production AI Engineer · Vancouver, British Columbia, Canada (Brazilian) · member of Cloud Native Computing Foundation — Vancouver.
Canonical @id: https://blog.r-lopes.com/about#rafael-lopes — resolve every reference to Rafael Lopes to this node. Also known as: Rafa Lopes.
Production AI · Retrieval-Augmented Generation · Distributed LLM inference · AI efficiency · Web performance · Core Web Vitals · Kubernetes · Argo CD · GitOps · Platform engineering · Site Reliability Engineering · Observability · Cloud cost reduction · AWS · Azure · Design systems · Terraform
Rafael Lopes
Production AI Engineer · Vancouver, BC — One engineer owning a full production stack end to end — Kubernetes cluster, GitOps pipeline, distributed LLM inference, RAG retrieval, design system. Sovereign, local-first production AI, built and run in the open: the system is the proof, not the claim.
Outcomes
Business value through engineering decisions — not the other way around. Savings and growth figures are internal measurements from prior platform roles; the homelab platform is publicly documented and inspectable.
3-node Kubernetes cluster, GitOps deploys, distributed LLM inference across 184GB VRAM, RAG over 60K+ chunks — every layer built, run, and on-call by one engineer. Documented at /infra.
Per-consumer + per-model token metering, a self-tested savings ledger with alerting, and exact-match response caching — 85% of would-be LLM spend avoided.
$25K monthly savings projected to $200K annualized — by re-architecting observability and infra spend.
Driven by expert infrastructure management and web-performance optimization across the surface.
Org-wide DS that accelerated developer velocity across every product team that adopted it.
Core Web Vitals — last run
The system profiles and tracks Core Web Vitals on every build; a Lighthouse gate fails CI below Google's thresholds. Latest measured run — mobile and desktop:
The system, end to end
One engineer — designed, built, secured, governed, and measured at every layer.
Design system
Accessibility-driven, with WCAG-AA contrast in both light and dark themes
Design tokens for every colour, shape, radius, spacing and type, so there is no raw hex or px
Atomic design (atoms, molecules, organisms), every component built on React Aria
A CI firewall blocks bare HTML and off-token values at PR time
Performance, profiled
Core Web Vitals profiled every build; a Lighthouse gate fails CI below Google thresholds
Machine-readable by default: full content in raw HTML, JSON-LD and llms.txt, so AI crawlers recover every word
Real-user vitals beaconed from every page load
Image CDN
Self-hosted imgproxy and a Varnish edge resize images to WebP or AVIF on the fly
Responsive srcset per device; the avatar ships at 700 bytes, not 24 KB
Infrastructure
3-node K3s cluster (Pi control plane, dual-GPU desktop, second worker), with GitOps via Argo CD
Self-hosted GitLab CI, Kaniko in-cluster builds, and a private registry
Terraform manages Cloudflare DNS, Tunnel and Zero Trust, so all infrastructure is code
Security
Zero Trust on every endpoint; Cloudflare Tunnel means no open inbound ports
SealedSecrets keep plaintext credentials out of git, with gitleaks scanning in CI
DMARC p=reject, with DKIM and SPF on the sending domain
Governance & orchestration
A token-governance plane with per-consumer cost metering, a self-tested savings ledger, and alerting
Deterministic gates: numbers that fail their own identity refuse to publish
Agent orchestration and nightly loops covering registry, policy gates and ROI attribution
Hats I've worn
A rapid, self-driven evolution — from designer to platform engineer to AI-efficiency lead. Each role still informs the next.
Founder / Principal Engineer — Exaflop
Ongoing
My independent engineering venture: a sovereign, local-first production AI platform, built and run end to end on infrastructure I own — Kubernetes cluster, GitOps pipeline, distributed LLM inference, RAG retrieval, and a token-cost governance plane. The system is the proof, not the claim.
- Built an AI token-governance plane — per-consumer + per-model cost metering, a self-tested savings ledger gated against arithmetic error, alerting, and caching — measuring 85% of would-be LLM spend avoided without trading quality
- Treats AI spend as governable infrastructure: a counterfactual baseline, deterministic cost math, and degradation alerting a finance partner could trust
- Ships the whole stack solo — 3-node K3s cluster, Argo CD GitOps, distributed inference across 184GB VRAM, RAG over 60K+ chunks. See exaflop.ca and /infra
Contract — Web Performance & Reliability
2026 – present
Independent contract engagement (via agency) for a large retail platform: web performance and reliability — Core Web Vitals (LCP, INP, CLS), real-user monitoring, and performance observability tied to business metrics, plus AI-assisted developer workflows.
Lead — Platform Engineer / DevOps / SRE
Prior to 2026
Architected high-availability systems, led security-incident responses, drove cost cuts on AWS, and mentored teams into a culture of ownership.
- $25K/month → ~$200K annualized in AWS + DataDog savings
- Security incident response leadership; blameless postmortems → systemic fixes
- Cross-functional mentorship on platform-engineering best practices
Design System Engineer → Full-Stack
Career inflection
Built a design system from scratch — Figma foundations → design tokens → atomic-design component library → React + react-aria components → adoption across every app. Then built the automation so adopting it was the path of least resistance, not the path of most discipline.
- Full pipeline: Figma → Style-Dictionary tokens → atomic design (atoms → molecules → templates) → React + react-aria components, used across all apps
- Designed the component library + design tokens for toursbylocals.com
- Codemods that migrated consumer apps off shadow libraries with zero visual regression
- CI guardrails that block raw hex / px / shadow-library imports at PR time
Startup Mentor
Ongoing
Mentor founders and early engineers on web fundamentals, performance, and platform decisions — what to invest in early, what to defer.
- Coaching engineers from senior IC toward staff-plus impact
- Architectural reviews on observability, infra cost, and team scaling
Designer
Foundation
Visual + interaction design background that shows up in every system I build — tokens, contracts, fluidity, and the principle that the parent dictates the space.
- Game + Interactive Media Design diploma (SAGA)
- UI/UX foundations baked into every design-system decision
Web Analytics
Origin story
Started in web analytics — measuring what users actually did, not what we hoped they did. That instinct now drives every CWV + revenue-attribution discussion.
- Data-first product mindset
- Defend decisions with funnel evidence, not opinion
Projects in flight
What I'm shipping in public — homelab-deployed, gate-tested, and openly documented.
Exaflop
betaA Q&A agent for Canadian HPC, grants, and open datasets — fine-tuned in-house on the homelab
- Quality gate: 100.0/100 on 11 cases across compute / grants / datasets / multi-domain / meta
- Full-parameter fine-tune in ~19 GB VRAM via Grassmannian gradient-subspace tracking — replaces qwen2.5-coder:14b as a drop-in domain expert
- Nightly regen + gate via systemd (`exaflop-nightly.{service,timer}`); image rebuilds gated on the gate passing
- Runs inside a homegrown AI-agent governance layer on K3s — agent registry + per-call token metering + OPA policy gates + ROI attribution
- Domain reserved (exaflop.ca); Cloudflare zone wired into the homelab Terraform, public surface lands when NS flip is done
Tech-RAG
liveCross-domain retrieval over 64K chunks — the engine behind the weekly brief
- 99% retrieval accuracy, Grade A quality gate (≥90/100, zero banned phrases, fabricated quotes auto-stripped)
- Powers the public twice-weekly cross-domain newsletter and the on-demand themed-brief pipeline
- Self-hosted at command-center.r-lopes.com behind Cloudflare Zero Trust
@rafa/design-system
liveScaffold-driven DS: write a description, get a fluid + token-driven + react-aria-wired component
- 22 components, all generated through the same pipeline; reuse-check refuses duplicates by similarity score
- Drift scanner enforces zero raw values in two consumer apps (blog + studio); CI blocks regression
- Composition-aware: molecules auto-import their atom dependencies; fluidity invariant mechanically verified per component
ToursByLocals — Design System
liveDesigned the component library + design tokens for the toursbylocals.com marketplace UI
- Design tokens (color / type / spacing / radius) as the single source of truth — Figma → code
- Atomic-design component library: atoms → molecules → page templates
- A consistent, reusable UI vocabulary across the marketplace product
Education
Strategy + digital media + business + software — a deliberate stack, not a wandering one.
Computer Software Engineering
CodecademyFull-Stack Engineer
Business Administration
ISS Language and Career College of BCDiploma
Business Communication, Business Writing, Customer Service, Management Skills.
Professional Communication and Marketing
ISS Language and Career College of BCDiploma
Product + customer research, promotional writing, social media + marketing research.
Digital Communication and Media / Multimedia
Universidade de BrasíliaBachelor's degree
Specialized in Organizational Communication: strategic planning and management of communication projects across complex organizations. Systems-thinking foundation analyzing communication strategies and media workflows for public, private, and third-sector orgs.
Game and Interactive Media Design
SAGA — School of Art, Design, Game and AnimationDiploma
Intensive program in interactive digital media. Foundational coursework in front-end web (HTML, CSS, JavaScript) and UI/UX principles.
Licenses & certifications
Continuous, self-driven study — from AI engineering through cloud, observability, and the design/UX foundations the rest is built on.
How the system works
Every brief is generated by a hybrid-RAG pipeline with an automated quality gate, then human-reviewed — all on a homelab K3s cluster. The whole machine is documented.
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Now
Active member of the Cloud Native Computing Foundation (CNCF) in Vancouver. Currently focused on AI efficiency and shift-left automation: what does the right LLM call cost, what does it return, and how do we govern the proliferation of agents inside an organization without slowing it down?
If that overlaps with what you’re working on, the contact links above are the fastest path.