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Aug 2026 to presentDATA & AI · Marketplace

iFood: Scaling AI Adoption Across 3,500 Engineers

First member of the DATA & AI department in Marketplace — the largest business unit at Latin America's leading food delivery platform. Capturing, connecting, and packaging AI patterns that emerge organically across 30+ squads.

Latin America's largest food delivery

3,500+
engineers in Marketplace
30+
squads experimenting with AI

The challenge

iFood’s Marketplace — the core food delivery product — runs on 3,500+ engineers. Every squad is experimenting with AI independently: some with Cursor rules, some with MCP servers, some with custom harnesses. The result is a patchwork of isolated wins. Nobody knows what works across the org. Nobody can replicate what one squad figured out.

The AI adoption mandate from the CTO is clear: everyone uses AI, it’s a directive, not an option. But directives without coordination become chaos.

The mandate: capture, connect, package

I’m the first member of the new DATA & AI department in Marketplace. My job isn’t to build AI products — it’s to make 3,500 engineers ship faster by turning scattered AI wins into common patterns.

The adoption flywheel

Input

Squad-level AI experiment

  1. 01Capture

    Enter each BU's active fronts, document what's working and what's blocking. Ads with internal AI coding platform + design tools, Pricing with pricing intelligence tools, Mobile with editor rules + custom MCP servers.

  2. 02Connect

    Link squads doing similar things in isolation. The Ads team doesn't know Pricing solved the same problem. My job is to make them talk.

  3. 03Package

    What proves good becomes common standard: harness configs, skill catalogs, ADRs, MCP servers. Practice becomes adoptable pattern.

Output

3,500 engineers ship AI-assisted code with shared patterns

The adoption flywheel: flow of 3 steps from "Squad-level AI experiment" resulting in "3,500 engineers ship AI-assisted code with shared patterns".

What shipped

First 30 days — groundwork

  • Required:
    Ads Portal pilot validatedFirst territory of execution. AI-assisted PRD creation, design integration, and review workflow established.
  • Required:
    Working group with DevXMulti-business-unit working group with the architecture enabler team. Presented to leadership.
  • Required:
    Network mappedConnected with 15+ key people across multiple business units and platform teams.
  • Required:
    Baseline instrumentedInstrumented baseline metrics against which adoption will be measured.

The 3-layer architecture I navigate

iFood runs on 3 structural layers plus cross-cutting concerns. My department cuts across all three:

Layer What it does My role
C1 — Value Generation (BUs) Where AI becomes money/product Measure results here — capture+connection appears in BU metrics
C2 — Capabilities Tech stack, platforms, AI tools Use, adapt, connect existing capabilities; package what works
C3 — Enablers Culture, way of work, disciplines Human connective tissue — network of AI champions

My fuel is C2. My terrain is C3. My result is measured in C1.

The iFood playbook: 4 strategies + 1 mandate

iFood’s tech culture runs on accumulated strategies. The AI adoption mandate is the newest, and the one where I operate:

  • Engineering resilience culture (est. 2020) — cybersecurity and resilience DNA. Not my responsibility, but my patterns must respect it.
  • Platform architecture discipline (est. 2020) — technical and organizational platform thinking. Not my responsibility, but it’s the structure I build on.
  • Small-team experimentation model (est. ~2021) — small teams testing theses fast. Phase 2 for me — after I prove capture-connect-package works.
  • AI adoption mandate (est. 2026) — my primary vector. The universal directive that everyone uses AI. My job is to make it coordinated practice, not chaos.

Stack

  • Cursor
  • Claude Code
  • AI coding platforms
  • Multi-model gateway
  • Internal harness frameworks
  • MCP servers
  • AI automation pipelines