I turn business need into software in production.
More than 15 years in technology, from 24×7 mission-critical infrastructure to AI product leadership. This is the technical record of what I built: the engineering problem, the decision that holds it up and what that decision cost to learn.
Who I am
Marco Gomes
I started in telecom infrastructure and moved through IT and cloud infrastructure. I led infrastructure and information security teams and ran data center migrations in 24×7 operations, the kind of project where mistakes are expensive and nobody notices when it goes right. IT coordination, service management and project and product management came next.
That background puts business, architecture, development, security and cost under a single head, which shortens the distance between the technical decision and the product decision. It is where the method comes from: the real pain first, execution afterwards.
Working thesis
AI products and products built with AI, taken from MVP to production with financial viability, security and scalability.
The work covers architecture, development, code validation, infrastructure and cost model.
Knowledge assets
Problems already solved and the reasoning behind each decision.
Systems I built from the first commit to production. Open each one to see the engineering problem, what was built and the decision that holds it up.
- Problem
- Operations that need to change conversation rules without waiting for a deploy, while keeping tenant isolation and an audit trail.
- What was built
- An engine where the flow is versioned data rather than code: a core isolated from channel and interface, persisted session and context, handoff to a human operator decided by the core, declarative RBAC and structured logging per node. Alongside it, a visual canvas flow editor with import of legacy diagrams and export to the target formats.
- Decisions behind it
- Backend-first: the interface only exists once core, database and audit are stable. The core decides, but never sends a message or calls an external API. And the tenant filter applies to every table and every query, no exceptions.
TypeScript · Node · PostgreSQL · React · WebSocket · REST
- Problem
- Connecting a modern voice channel (WebRTC with encrypted media) to platforms that speak SIP, within the RFCs, traversing NAT and without depending on a public IP.
- What was built
- A TypeScript control plane with signed webhook ingest, anti-replay, a per-call state machine, permissions, billing records and multi-tenant isolation through row level security. The media plane is purpose-built in Go and sits behind an interface: swapping the media implementation never touches signalling.
- Decisions behind it
- No media decision was closed before a real packet capture. The documentation separates what is implemented from what is proven, and that distinction is deliberate. Media invariants each carry an automated test, because violating any one of them drops the call silently.
TypeScript · Go · WebRTC/SRTP · SIP · PostgreSQL RLS
- Problem
- Identifying customers on voice channels accurately enough to automate the identification step, without turning a deterministic process into a recurring generative AI bill.
- What was built
- An understanding engine tailored to the domain, solving identification and the deterministic steps that precede it. The design came out of watching the real operation.
- Decisions behind it
- Where the process is deterministic, determinism is cheaper, faster and auditable. A generative model only belongs where there is genuine linguistic ambiguity.
NLU · Python · Intent recognition
- Problem
- Companies adopt AI without knowing how much they spend, who spends it and what the return is. The cost usually shows up after it has already been committed.
- What was built
- A multi-tenant platform that measures token consumption per person, team and cost center, applies rates and closes a monthly invoice. It also supports the skills catalogue, the adoption tracks and the usage governance, with analytics and roles defined per organization.
- Decisions behind it
- Explicit SQL, no ORM, and reversible migrations. Isolation lives in the database through RLS, on top of the filter in the application. Every mutation goes through audit. Data protection was there from day one, with export and anonymization, and authentication is pluggable.
TypeScript · Fastify · PostgreSQL · React · Cloudflare · Fly.io
- Problem
- Hundreds of pages of technical specification in PDF were converted by hand into the binary format of a plant CAD tool. That burned weeks of specialist work and produced errors that were hard to find.
- What was built
- A deterministic pipeline that goes from the PDF to the CAD files through an intermediate model. The vendor format was reverse-engineered from a working file. Validation stops and asks only what is genuinely ambiguous, every run produces a verification report and access is controlled by role.
- Decisions behind it
- Zero generative AI: the process is deterministic, and determinism can be audited. One single orchestrator, shared by the command line and the web interface. What restricts access is the route, because a button hidden in CSS restricts nothing at all.
Python · SQLite · PDF parsing · pytest
- Problem
- Recording and tracking clinical progress outside the clinic without creating a central repository of sensitive data in the cloud.
- What was built
- An application that keeps everything on the device, with pure and tested domain logic that is independent of interface and storage. Export is an explicit user action, and the same codebase is packaged for iOS and Android.
- Decisions behind it
- Privacy settled in the architecture before it becomes policy: there is no account, no backend and no collection. Derived data is always computed and never becomes the source of truth. The product only promises what the data model can hold up.
React · TypeScript · IndexedDB · Capacitor · Vitest
Areas of work
Five fronts, one engineering practice.
From discovery with the business teams to running in production. These are the fronts I work in and where the experience runs deepest.
Applied AI engineering
LLM integration into products, agent orchestration, RAG, prompt engineering, model evaluation and cost-per-token control. That includes saying plainly when generative AI is not worth it.
Agent-assisted development
Building products and MVPs with coding agents, defining architecture, writing and validating code and provisioning infrastructure up to go-live. Review and testing stay a human responsibility.
Discovery and turning business need into product
Mapping real pain with the operations, sales and customer service teams, with use cases prioritized by impact, effort, risk and value. The scope comes out of that conversation.
Solution architecture and technical review
Domain, data, integrations and isolation designed before the code. Real multi-tenancy, row level security, audit trails and decisions recorded as ADRs.
Cloud, DevSecOps and continuity
Cloud architecture, delivery pipelines, observability, security policies and contingency and disaster recovery plans, always with operating cost in the equation.
How I work
From the real pain to cost under control.
Pain before solution
The problem is mapped alongside the people who live it, in operations, sales and customer service. The scope comes out of that conversation.
Decisions on record
Domain, data, integrations, isolation and cost model defined and written down before the first line of code.
From MVP to production
Agent-assisted construction, with human review, tests and incremental delivery. Speed without giving up validation.
Cost under control
Observability, security, continuity and unit economics stay tracked well after go-live.
Engineering principles
Technical opinions that were expensive to form.
Every rule below came out of a system in production and out of at least one mistake that paid for the lesson.
Stack
The right tool for the problem.
The stack is chosen by need and best cost-benefit, with attention to scalability, security and operating cost.
Construction assisted by coding agents. Architecture, review and testing stay under human responsibility.
Track record
From mission-critical infrastructure to AI product leadership.
By period, responsibility and industry. Company and product names are left out: what matters here is the kind of problem faced.
AI and CX product management
Full lifecycle of AI products, from ideation with the business teams to production: development with AI agents, applied AI engineering (LLMs, agents, RAG, evaluation and cost per token), architecture and DevSecOps, pricing, roadmap and KPIs. Two products taken from zero to production, both with an active sales pipeline.
Project management
Scope, schedule, risk and stakeholder communication, with responsibility for financial planning and control, including budget, forecast, variance analysis and cost optimization, plus vendor management and strategic meetings.
IT coordination
Leading the technology team and the digital transformation agenda: process automation, service digitalization and self-service. Infrastructure and information security with governance and regulatory compliance, omnichannel orchestration, cloud migration (AWS, Azure and GCP), IT budget, contracts and business continuity and disaster recovery plans.
IT service management
Operational excellence with ITIL and governance: SLAs and KPIs, service desk, RPA automation, infrastructure and cloud, cybersecurity, change management, proactive monitoring and APM, plus leadership of multidisciplinary teams.
Telecommunications coordination
Telecom infrastructure for mission-critical operations with high availability and low latency. Network and telephony modernization (VoIP and cloud PBX, SD-WAN), contact center solutions with IVR and messaging, NOC leadership, contingency and link redundancy, carrier negotiation and regulatory compliance.
Telecommunications analyst, mid and senior
Administration of LAN, WAN, Wi-Fi and MPLS networks; VoIP and PBX, including cloud-based; support for contact center platforms (IVR, call recording, IP telephony); proactive monitoring, SLAs, carrier contracts and contingency plans.
Start in IT
Installation, configuration and maintenance of corporate telephony platforms and wireless networks in large-scale environments.
MBA · Master in Information Technology
Information Technology
Systems Analysis and Development
Bachelor's degree in Information Technology
Industrial Electronics
Technical degree in Electrical and Electronic Engineering
Contact
Where to find me.
This site is a technical record, not a commercial proposal. If you want to talk about any of these experiences, LinkedIn is the shortest path.