AI & Enterprise Architect specialized in designing scalable, cloud-native and agentic AI architectures — bridging business strategy, emerging AI technology, and enterprise execution.
At BBVA I lead the definition of the bank's AI & Cloud architecture strategy — sitting at the intersection of business strategy, technology architecture, and innovation, not just implementation. 13+ years moving from software developer → IT architect → architecture manager → AI & Enterprise Architecture lead gives me both the boardroom fluency to align AI initiatives with business goals and the hands-on depth to prototype what I propose before asking a team to build it (see Featured Projects — my public lab; most of my enterprise architecture work at BBVA is confidential).
| Stage | Focus |
|---|---|
| Now — AI & Enterprise Architecture Lead | AI strategy, agentic & cloud-native architecture, business-technology alignment, team leadership |
| Manager of Architects | Core banking, service architecture, enterprise solutions across business units |
| IT Architect | SOA, microservices, FaaS/serverless, cloud computing |
| Software Developer (foundation) | Java, C#, PHP, Python — the hands-on base my architecture decisions still draw on |
| Project | What it does |
|---|---|
| ai-video-dubbing-pipeline | 100% local, open-source video dubbing (EN→ES): Whisper transcription, pyannote speaker diarization, context-aware LLM translation, per-speaker voice cloning. No paid APIs. |
| prometheus-inference-platform | Self-hosted LLM inference gateway — JWT auth with per-model scopes, rate limiting/circuit breakers, multi-backend routing (llama.cpp/MLX/vLLM/SGLang) across distributed hosts, full OTel tracing. |
| agentic-doc-intelligence-platform | Document intelligence for payslips/insurance docs: bounded ReAct extraction loop, 6-category deterministic validation, pluggable OCR, full observability. |
| synaptum-framework | Minimal, bus-driven framework for multi-agent orchestration — decoupled messaging and versioned prompts, vendor-neutral by design. |
| axonium-sdk | Python SDK for llama-server: auth, token rotation, PII masking, and Langfuse observability for production LLM systems. |
(These are my public lab — where I test the AI architecture ideas I take into BBVA. Pinned on my profile; check the repo list for more.)
Building hands-on — evidenced in the projects above
Applying at architecture & leadership level — real professional experience, not shown in public code
Foundation — earlier career
- 🏛️ Enterprise Architecture — TOGAF-aligned strategy, bridging technology and business goals across a regulated financial institution.
- 🧠 AI & Agentic Architecture — designing agentic systems, RAG/hybrid retrieval, multi-agent patterns, and LLM integration strategy — from proof-of-concept to enterprise rollout.
- ☁️ Cloud Strategy — multi-cloud architecture (AWS/Azure/GCP), cost optimization, and cloud-native/serverless design.
- 🔗 Integration Patterns — SOA, EDA, Event Sourcing, EIP for complex, high-availability enterprise systems.
- 🖥️ Hands-on LLM infrastructure — self-hosted inference gateways, multi-backend routing, quantization (Q4/Q5/Q6) — demonstrated in shipped open-source projects.
- 🔐 Security & Compliance — secure architectures for regulated industries: JWT/RBAC, PII masking, guardrails, cryptography.
- 👥 Technical Leadership — building and mentoring the team that defines BBVA's AI & Cloud architecture strategy.
Already applying, going deeper on:
- Multi-agent orchestration at enterprise scale, and what "AI-native" enterprise architecture looks like when agents — not services — are the unit of design.
- Small language models & edge/local inference as a deliberate strategy, not just a cost hack.
Actively studying — genuine gaps I'm closing, relevant to banking/regulated AI:
- Agent identity, governance & audit-trail frameworks for autonomous systems.
- EU AI Act & DORA compliance requirements for agentic architectures in financial services.
- FinOps for AI — GPU/token cost governance at production scale.
- Context engineering as a discipline distinct from RAG.
- Sovereign AI & data-residency patterns for regulated, multi-region deployments.
Continuous learning, open-source AI tooling, and mentoring engineers moving from development into architecture.