01
The system is growing faster than its controls.
Payments volume, integrations, incidents, and compliance obligations are increasing—but architecture and ownership have not caught up.
Fintech engineering scale / AI modernization / US–LATAM
Engineering judgment for the next stage.
Principal-led engineering consulting for fintech and payments companies scaling architecture, distributed systems, teams, or AI infrastructure across US–LATAM markets.
Principal experienceAmazonMetaMicrosoftFélix PagoNubankLyft
The inflection point
Growth exposes the operating system.
Most fintech teams do not need a generic transformation. They need a clear answer to a consequential question: what should change now, what can wait, and who owns the next move?
02
The team is busy, but delivery is harder to explain.
Roadmaps slip, decisions linger, hiring feels improvised, and the board wants a credible engineering plan before the next stage.
03
AI adoption is happening without an operating model.
Engineers use agents, but quality gates, security boundaries, measurement, and review practices remain implicit.
Relevant operating proof
Built inside the constraints—not from the sidelines.
Operating leadership across cross-border payments, global-scale distributed infrastructure, US–LATAM team building, LLM systems, and public open-source work on AI-agent safety.
Read the fintech scale case →$250M+
monthly payments volume supported
Mauricio / Félix context
99.999%
global infrastructure reliability
Francisco / Meta context
40+
person engineering organization led
Mauricio / Félix context
Millions/sec
global CDN request scale
Francisco / Microsoft context
Offer ladder
Diagnose first. Implement second. Embed only when needed.
01
Entry product
Fintech Engineering Scale & AI Readiness Assessment
A jointly scoped review of architecture, distributed systems, delivery, team design, security, and AI workflows—ending in a ranked risk register and 90-day plan.
→02Focused implementation
AI engineering workflows
Guardrails, review standards, agent policies, and measurement for teams adopting AI-assisted development.
→03Focused implementation
AI infrastructure, LLM & RAG systems
Evaluation, retrieval quality, observability, performance, cost, security boundaries, and production reliability.
→04Focused implementation
Payment systems architecture
Transaction state, ledgers, retries, reconciliation, reliability, observability, and staged modernization.
→Joint principal engagement
Scoped proposal
quoted jointly · typically 3–4 weeks
Fintech Engineering Scale & AI Readiness Assessment
Architecture, delivery, team structure, security posture, AI workflows, and diligence readiness—translated into a prioritized risk register and an executable 90-day plan.
The principal team
Two operators. One accountable company.
Every engagement is scoped, quoted, and reviewed jointly. Mauricio leads the business, payments, organization, and execution lens; Francisco leads distributed systems, reliability, performance, observability, and AI infrastructure depth.
Principal 01
Mauricio Perez Romero
Engineering Principal — Fintech, Payments & Engineering Scale
Fintech engineering leader with 13+ years across payments, credit, distributed systems, data platforms, hiring, and US–LATAM engineering organizations. He currently serves as Head of Engineering at Winston Artory Group after operating roles at Félix Pago, Nubank, Lyft, Oracle, and Nextiva.
- $250M+ monthly payments volume supported
- 40+ person engineering organization led
- AI-agent safety tools built and maintained in public
Principal 02
Francisco Perez Romero
Engineering Principal — Internet-Scale Systems & AI Infrastructure
Francisco has spent 18+ years engineering systems at a scale few teams ever encounter. Now a Senior Software Engineering Manager at Amazon, he previously built and led infrastructure at Meta and Microsoft that served billions of users and handled millions of requests per second. At Félix Pago, he applied that depth to production LLM evaluation, RAG, observability, and AI performance.
- Infrastructure serving Facebook, Instagram, WhatsApp, and Messenger worldwide
- Reverse-CDN systems handling millions of requests per second
- Global routing and telemetry infrastructure built for 99.999% reliability
- Engineering team scaled from 4 to 15+ while remaining hands-on
Employment history identifies relevant individual experience and does not imply endorsement of Fintech Field Office by any current or former employer.
Useful before a sales call
Interactive diagnostic / 8 dimensions
Fintech Engineering Scale Scorecard
Identify the weakest operating dimension and get a prioritized next-step recommendation. No email wall.
Run the scorecard →Public proof / AgentLint
Guardrails for AI-assisted engineering
A concrete look at 77 public rules for quality, security, and infrastructure safety—without an invented velocity claim.
Examine the proof →Evergreen decision library
Answer the buying question before the call.
Original guidance on fractional leadership, technical diligence, payment-system scale, and production AI infrastructure—written from the operating evidence behind the practice.
Assessment vs. CTO
Choose diagnosis or embedded leadership from the decision at stake.
Read →Technical diligence
Prepare architecture, controls, evidence, and ownership.
Read →Payment scaling
Review invariants, reconciliation, reliability, and operations.
Read →Production LLM/RAG
Evaluate quality, retrieval, observability, cost, and security.
Read →AI agent controls
Inspect public guardrails for permissions, quality, security, and completion.
Read →Fractional CTO cost
What the engagement model and investment should actually buy.
Read →Selected field work
Working products, not presentation artifacts.
A small portfolio of public tools and product experiments built to test engineering, distribution, and workflow ideas in the real world.