Fullstack AI Engineer · 8+ yrs

Value
driven
agentic
systems.
Built to
scale.

Governable as they grow. I learn the need, find where the value is, and challenge the process.

A mock-up before the build. Production once the value is proven. A handover so the team keeps the capability.

$ am --engage >> BRING A PROCESS
Available · Madrid
Studio
AI & Engineering, Madrid
8+ yrs
From squad lead to production AI
Range
Low-code validation to full-stack agents
Trust
EU AI Act, GDPR, and NIS2 in the build
A-01 · SETUP

The setup, before the agent.

An agent for a consulting firm starts as a drawing. The work they already do, the sources and rules, the agent, and the team that will run it.

01 THE WORKBRIEFSOURCESCASEDOCDATARULES02 AGENTAGENTROUTETOOLSTRACES03 THE FIRMDRAFTREVIEWTHEY KEEP ITCONSULTINGSETUPHANDOVER

Bring the situation,
not the tool.

// WHERE YOU ARE
YOU LEAVE WITH THE PROCESS

There is an idea for a tool, and no process behind it. We start where the work happens and leave with a problem statement and the value at stake.

// 01 — METHOD

How an engagement runs

Three steps. The need picks the tool, from low-code to full-stack. Business first, technology second.

01⌖

The process

Bring one process where the number has not moved, or one pilot that is stuck. You leave with a problem statement and a decision to scale or stop. If the value is unclear, the build waits.

Value firstAs-isOne process
02◇

The build

A mock-up before a line of code. Then the data and the agent, from low-code validation to full-stack production engineering. Routing, traces, and a KPI agreed before the sprint.

Mock-upAgentsTracesKPI
03▷

The handover

The people who do the work use it in daily operations. You keep the capability. I do not run the tool after go-live. Human oversight stays in the loop.

AdoptionHandoverEU AI Act
// 02 — PROOF

What the work looks like

01
HazelHeartwood · 2025—present

AI & Engineering Studio

I lead the studio that puts agentic systems into daily operations. The firm's public stories — planners, supplier search, governed data — are HazelHeartwood's. This is the practice I run from Madrid: mock-up, production, handover.

TransformationAgentsHandoverMadrid
Live
in daily operations
Studio impact stories ↗
02
Interactive AI · 2025—present

Agent engineering platform

The production system behind the studio's engineering: observability and orchestration from zero to enterprise in under six months. A telemetry engine tracing every run, an MCP orchestration layer, and an LLM router across 200+ models.

FastAPIClickHouseMCPLLM RouterKubernetes
<6mo
zero to production platform
Visit interactive.ai ↗
03
Burlingame Studios · 2021—2025

A decade of shipping

Before the studio: fullstack and technical lead, including a CMS and live-ops tooling for an AAA mobile game serving millions of players, and a squad of five taken from business strategy to delivered software.

Technical LeadCMSLive OpsSquad of 5
8+ yrs
strategy to shipped software
Visit Flourish ↗
// 03 — TRAJECTORY

The path so far

A decade of shipping, then the studio. Select a year to read the role.

2025 — Present · Madrid, ES

Lead, AI & Engineering Studio

HazelHeartwood ↗
▌CHAPTER 06/06
AI CONSULTING · STRATEGY → IMPACT
→Guide human organizations through AI-driven change — from roadmap to measurable impact.
→Design and build complex multidisciplinary agent-orchestration architectures and systems.
→Consult, implement, and embed AI agents and systems into production within large enterprises.
→Translate business strategy into pragmatic, compliant agentic architectures.
→Partner with internal teams to upskill engineers and de-risk adoption at scale.
AI StrategyAgent OrchestrationAgentic SystemsEnterprise
2016
IB Diploma Programme
SEK International School, Ecuador
2017—2020
BSc Computer Science & Engineering
Universidad Francisco Marroquín · DevOps, Cloud & Web
2022—2023
Master in Management — STEM
EAE Business School, Madrid
// 04 — THE BUILD

What the engagement stands on

Low-code where the data already lives. Full-stack where the company needs to own the system. Traces and a human in the loop either way.

AI & Agents15
MMCPAAG-UILLLM RoutingLLangGraphLLangChainOOpenRouterOOpenAI APIAAzure AI FoundryGGoogle AI StudioLLangfuseRRAGVVector SearchCCLI DevelopmentAAI-Native CI/CDCCoding Agents
Frontend05
NNext.jsRReactGGenerative UITTailwindAAngular
Backend & Languages06
GGoPPythonFFastAPINNode.jsTTypeScriptPPHP
Cloud / IaC08
TTerraformKKubernetesDDockerAAWSGGCPAAzureGGitHub ActionsGGitLab CI
Data Stores06
CClickHousePPostgreSQLRRedisMMySQLMMongoDBDDynamoDB
Compliance & Trust04
IISO 27001SSOC2GGDPREEU AI Act
// 05 — NOTES

The method, in writing

Read all writing →
June 25, 202618 min read

Building an AI Engineering Practice — A Reference Strategy

A generalized, anonymized playbook for turning a salad of one-off agents and tools into a standardized AI Engineering platform — one that builds from a compounding set of reusable blocks, so every next solution ships faster, safer, and cheaper than the last.

AI EngineeringStrategyPlatformAgentsEU Sovereignty
// 06 — ENGAGE

Bring me the process.

One hour. Bring one process where the number has not moved, or one pilot that is stuck. You leave with a problem statement and a decision to scale or stop.

am@madrid ~ /contactavailable
$ am --engage --process
→ one process, or one stuck pilot. The first conversation is an hour.
$ _