Senior Technical Writer • AI workflow design • Knowledge systems
I help teams find context faster, make better decisions, and reduce repetitive operational work. My work combines AI-assisted workflow design, information architecture, support-data analysis, and technical documentation—grounded in five years working across enterprise networking, cloud security, and developer-facing products at Cloudflare.
What set him apart was how he thought about the work around the writing.
Where I add value
I turn repetitive knowledge work into reusable AI-assisted workflows for research, analysis, auditing, migration planning, review, and operational decision-making. The focus is not isolated prompts, but reliable systems with clear source material, rules, validation, and human review.
I structure fragmented product, support, and operational knowledge so that people and AI systems can retrieve it, understand it, and act on it. This includes information architecture, taxonomy, content models, source-of-truth design, and knowledge-maintenance workflows.
I design documentation, troubleshooting systems, and decision guides for complex technical products. I use support data and customer journeys to identify where users are getting stuck and build maintainable systems that reduce confusion and unnecessary escalation.
Impact, by the numbers
Documentation systems, operational tooling, and support-driven workflows, with measurable outcomes.
Research reduced from days to ~60 minutes
Designed reusable AI-assisted workflows for research, auditing, migration planning, review triage, documentation quality checks, and structured drafting. The result was not just faster writing, but shared infrastructure for repeatable documentation work across multiple teams.
Read the full case study →695 files in 3 days
Reduced a projected multi-week migration into a 3-day operational workflow by automating documentation updates across 695 files, 349 URL mappings, and hundreds of supporting assets.
Read the full case study →753 support cases analyzed
Analyzed support escalations across the product area's largest support category and built a four-guide troubleshooting suite for enterprise networking products, reaching thousands of views in the first three months.
Read the full case study →Weekly production digest
Built automated reporting workflows that surfaced stalled pull requests, tracked queue state, and improved review prioritization for the documentation team.
Read the full case study →Practical AI work
I use AI to turn real operational problems into working tools. These projects demonstrate product judgment, workflow design, model orchestration, iteration, and the ability to ship—not just prompt-writing.
Local and cloud models, source aggregation, fit evaluation, ATS scoring, and application-state tracking in one job-search workflow.
Public transport data, API proxying, and a live installable web application for Lisbon Metro arrivals.
Scheduled wildfire monitoring, distance and wind-based risk evaluation, and targeted notifications.
In their words
The consistent theme in peer feedback: my value is not only the documentation I publish, but the systems, workflows, and judgment I bring to the work around documentation.
He's the kind of writer who is constantly pushing the envelope for what a technical writer can do. He built a full AI toolkit — agents, skills, custom commands — that connected MCP servers, documentation sources, and GitHub into workflows that let writers move through research, drafting, and publishing dramatically faster. Proactive, reliable, and completely unfazed by ambiguity. I'd work with Márcio again without hesitation.
He's proactive — for every feature I built, he asked about documentation needs before it even crossed my mind. He's organized, keeping careful track of release dates, and very good at quickly learning new tech all across our product suite. Márcio also invented workflows to use LLMs for large docs refactors that would have been tedious and error prone by hand. And to top it all off: he's one of the kindest people I've worked with — you want him on your team!
Featured writing
Selected posts on technical writing, AI workflows, and documentation systems.
I've spent quite a bit of time building AI workflows and learned one thing that immensely improved the quality of my work: AI is blind to its own errors and needs a pass from a skeptical agent to catch mistakes.
The healthy response to losing a job is probably long walks and a journal. I opened a code editor and started building a game where a little sprite dodges things labelled AI SLOP. I think this game will resonate with a lot of people. It's silly, but plenty of us have been through the same thing, and the anxiety under it isn't only mine.
The pitch sells itself. An assistant that's entirely mine, running on my own machine, needing no connection, with nothing I type ever leaving the room. No company counting my tokens. No subscription. No outage on someone else's servers wrecking my afternoon (I'm looking at you, Anthropic, and your recurring outages). I wanted that badly enough that I spent a few months chasing it, and I want to tell you honestly where it left me.
About
Developer documentation has moved from reference material to infrastructure. It is increasingly the interface that customers, support teams, internal tools, search systems, and AI agents use to understand products and make decisions. That means documentation needs to be intentionally architected for retrieval, clarity, reliability, and maintenance, not simply published. Most documentation systems are not designed for that reality yet.
At Cloudflare, I built the systems to close that gap: AI-assisted workflow tooling adopted across multiple teams, support data analysis pipelines that surfaced where customers were actually struggling, troubleshooting architecture redesigned from real escalation patterns, and operational infrastructure that compressed multi-week projects into days. That machinery — the tooling, the support-data pipelines, and the review structures — is what I spent my time on, and it kept the documentation accurate as products changed.
I started in technology journalism, which trained the instinct that still shapes everything: the gap between what organizations know internally and what users actually understand. That gap is what I'm paid to close.
In practice, that means treating documentation as part of the product experience itself: something that directly affects adoption, support volume, developer trust, and how effectively both humans and AI systems can use a platform. The goal isn't better writing. It's better knowledge infrastructure.
Role fit
Contact
I’m looking for roles where I can combine technical communication, AI-assisted workflow design, knowledge architecture, and operational problem-solving. I’m particularly useful in organisations with complex products, fragmented information, recurring support friction, or teams beginning to introduce AI into knowledge-heavy workflows.