I turn models into shipping systems.
CTO at Of Them All and founder of Illuma. I build AI workflows, niche models, and platform infrastructure for domains where judgment, latency, and trust matter.
years turning ambiguity into production systems
markets across Asia and Europe with different operating constraints
engineers hired, coached, or mentored across distributed teams
AI-assisted audio mix workflow, down from a 15-minute manual pass
Useful AI is not a feature. It is a production system with evidence, workflow, and accountability around it.
I built payment and marketplace infrastructure when uptime, fraud, compliance, and millions of daily transactions were the product. That operating muscle now shapes how I build AI systems.
The pattern is consistent: find the expert bottleneck, model the decision path, wire the data layer, then harden the workflow until a team can trust it in production.
Practical AI, wired into the systems that make it useful.
The work is not just prompting a model. It is designing the data path, product loop, delivery system, and operating cadence around it.
Agentic workflow design
I turn repetitive expert work into supervised AI workflows: tool calling, review gates, retrieval, and measurable human-in-the-loop throughput.
RAG and model adaptation
Domain corpora, embeddings, retrieval strategy, prompt contracts, evaluation loops, and targeted fine-tuning when the base model is not enough.
Platform architecture
Architecture for systems that must keep moving: payments, marketplaces, automation backplanes, queues, observability, and deployment paths.
Engineering operating system
Hiring, coaching, SDLC, delivery cadence, incident habits, and the technical narrative needed when teams and products need to mature fast.
From model possibility to operational habit.
I prefer short loops, visible risk, and working systems over long speculative roadmaps.
Map the bottleneck
Identify where expert judgment, latency, or repeated decisions constrain growth.
Prototype the loop
Build the smallest model + data + interface path that can prove useful work.
Harden the system
Add retrieval, evaluation, permissions, observability, and human review where risk demands it.
Ship the habit
Turn the workflow into team behavior with metrics, ownership, and iteration cadence.
Ventures as live laboratories, not portfolio decoration.
Each product is a place to test the same thesis: specialized AI only matters when it is embedded in the workflow and accountable to outcomes.
Illuma
Legal AI · illuma.law
A legal AI platform for evidence management, precision case analysis, and specialized workflows that need traceability instead of generic chatbot behavior.
Luminous Giant
AI systems consultancy · luminous-giant.com
A build practice for founders and operators who need AI workflows, platform architecture, and engineering execution in the same room.
AI systems are layered products.
The useful work happens where models, data, product logic, and operational discipline meet.
Model layer
LLMs, Gemini, prompt contracts, evaluation sets, fine-tuning, agent behavior, and task decomposition.
- LLMs
- Agent workflows
- Fine-tuning
- Prompt engineering
- Model evaluation
Knowledge layer
The retrieval and memory plane that keeps AI grounded in product, legal, media, or operational context.
- RAG pipelines
- Embeddings
- Vector search
- PostgreSQL
- Meilisearch
Application layer
The product systems where AI meets users, permissions, payments, workflows, and business-critical state.
- Laravel
- TypeScript
- Cloudflare Workers
- Microservices
- Payment systems
Operations layer
The delivery environment: CI/CD, containers, observability, incident loops, and teams that own production.
- Docker
- Kubernetes
- AWS
- Linux
- GitHub Actions
Career proof across marketplaces, payments, ML platforms, and teams.
Expand each row for evidence, highlights, and technologies from the role.
People who have seen the operating system up close.
“Having worked 10 years ago and again hiring Miguel to work closely with me for the past 4 years tells a lot about how much trust and respect I have for him. Miguel Enes has a very entrepreneurial mindset that always sets him to find solutions and to move organisations forward. He does that relying on his profound and complete tech knowledge and on-hands experience, leading by example/execution and supporting his team along the way. I highly recommend him as an autonomous, energiser, tech-savvy and delivery oriented Engineering Manager.”
Recent writing from the build floor.
What I'm Building in 2026
I am building new ventures in Portugal.
From Silence to Symphony: How We Built an AI-Powered Audio Mixing Engine with FFmpeg 8 and Intelligent Agents
We combine AI models, vector memory systems, and audio engineering rules to mix meditation mantras.
Orchestrating Multimedia Magic: How I Built Content Generation with Vizra ADK Workflows
I use Vizra ADK workflows to orchestrate multi-modal content generation across text, audio, and visual agents.
Bring me in when the prototype needs to become a system.
Useful conversations: AI workflow builds, legal-tech systems, marketplace/platform architecture, engineering leadership, and technical strategy for venture teams.