Adam
Ramirez

A 17-person team across 3 geographies, 28 global labels waiting on a platform rollout, and an AWS migration that had been stalled for a year. The org needed to ship more with less — not just cut costs, but fundamentally change how work gets done.

Restructured from 17 to 10 with zero delivery disruption. Introduced an autonomous bets model: 2-person pods, 1-week timeboxes, formal gates. Trained 100+ engineers on AI-first development — not just tools, but the workflows, specs, and judgment that make AI actually accelerate a team. Unblocked the AWS migration and rolled the platform out to 23 of 28 labels.

82% of global labels live. Moving to a 5-person strike team model where each pod ships end-to-end in a week. The operating model compounds — every sprint's learnings feed into constraints, specs, and decision logs that make the next sprint faster.

This isn't a story about cutting headcount. It's about proving that a small, AI-augmented team with the right operating model outperforms a large team running on inertia. The practices I develop at my startup are what I bring to the org — battle-tested, not theoretical.

Customer success teams lose winnable accounts because they can't synthesize signals fast enough. Usage drops, support tickets spike, renewal dates approach — and no one connects the dots until it's too late.

I built this solo while leading engineering at UMG. Multi-agent orchestration over monolithic prompts. Cost-aware routing across 37 models instead of one-size-fits-all. A 5-layer evaluation system to catch hallucinations before they reach customers. Every architectural decision optimized for trust at enterprise scale.

Enterprise pilot running on 1,200+ accounts. 6K+ tests. Token costs 10x lower than brute-force through complexity-aware routing. Production guardrails that pass enterprise compliance review.

This isn't a side project — it's where I develop the AI practices that scale. Autonomous bets, agent-native documentation, evaluation-driven quality. Everything I built here became the operating model I now teach to 100+ engineers at UMG.

The monolithic architecture had become the bottleneck for a Flutter app serving 50K+ users. Every deployment took 2–3 hours, required manual coordination, and carried real risk of user-facing outages. Feature velocity had stalled.

Architected and executed a full migration to microservices with containerized deployments. Ran both systems in parallel during the transition to protect the user base, and built the CI/CD pipeline and observability layer to support the new architecture.

Deployment time dropped from 2–3 hours to 15 minutes — a 92% reduction. Maintained 99.99% uptime throughout the entire migration. Zero disruption for the 50K+ user base.

Unblocked the product team entirely. Feature releases went from monthly events to weekly iterations. The reliability track record through the migration built deep trust with stakeholders and set the technical foundation for the next phase of growth.