将模糊概念转化为生产系统的经验年限
在亚洲和欧洲不同运营约束下的市场
在分布式团队中招聘、指导或培养的工程师数量
AI辅助音频混音工作流，从手动15分钟缩短
有用的AI不是一个功能。它是一个围绕着证据、工作流和问责制的生产系统。
我曾构建支付和市场基础设施，那时正常运行时间、欺诈、合规性以及数百万的日常交易就是产品本身。这种运营经验现在塑造了我构建AI系统的方式。
模式始终如一：找到专家瓶颈，建模决策路径，连接数据层，然后强化工作流，直到团队能在生产环境中信任它。
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.
从模型可能性到操作习惯。
我偏爱短周期、可见风险和可工作系统，而不是漫长、投机的路线图。
绘制瓶颈
识别专家判断、延迟或重复决策限制增长的环节。
原型迭代
构建最小的模型+数据+接口路径，以证明其有用性。
强化系统
根据风险需求，添加检索、评估、权限、可观测性和人工审查。
形成习惯
通过指标、所有权和迭代节奏，将工作流转化为团队行为。
创业项目是活生生的实验室，而非简历装饰。
每个产品都是检验同一论点的场所：专业AI只有嵌入到工作流中并对成果负责时才有意义。
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
横跨市场、支付、ML平台和团队的职业证明。
展开每一行以查看该角色的证据、亮点和技术。
亲眼见证过我运营能力的人。
“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.”
近期来自构建一线的文章。
What I'm Building in 2026
I am building new ventures in Portugal.
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