Descripción

I am an AI Engineer with a hybrid focus: I combine software architecture (Full Stack) with 14 years of experience in signal processing and audio.

My differentiator is both technical and creative: I apply the discipline of audio engineering: signal management, structured/unstructured data, and flow precision to build reliable data pipelines, optimize inference latency, and fine-tune models.

I understand that data acts as a complex signal (voice, video, text) requiring specific treatment. I design multimodal systems and automations that understand the nature of creative content to solve real business problems.

PROFESSIONAL IMPLEMENTATIONS

Enterprise Workflow Automation: Engineered automation infrastructure for high-volume content operations.

*Workflow Logic: Built modular n8n + Python frameworks to automate linguistic scoring and dataset integration.

*Data Exploration: Deployed dynamic query systems turning JSON schemas into SQL, allowing teams to query data without writing code.

*Governance: Implemented LLM-based logic gating to validate content against legal rubrics before shipping.

Media AI & RAG Systems Engineered AI-dubbing and search tools for large-scale EdTech platforms.

*AI Dubbing: Built a scalable pipeline (Voice Cloning + Lip Sync) to automate content localization into multiple languages.

*Semantic Search: Implemented LangChain + FAISS RAG pipelines over 300+ scripts for instant asset retrieval.

*Orchestration: Deployed Dockerized FastAPI services to chain Text-to-Speech (TTS) microservices.

Agentic Systems

*Designed autonomous Multi-Agent Systems using LangGraph to transform LLM outputs into executed business actions.

*Fine-tuned models using iterative feedback loops to improve accuracy in production environments.

STACK

Python,TensorFlow, PyTorch, Scikit-learn, Transformers, LangChain, LangGraph

GPT/Claude/Gemini, LLaMA, DistilBART, Azure, GCR, Docker, Git/GitHub, Cursor, VSCode, n8n, Make, FastAPI, REST APIs, ETL, DataWarehouse, Data Mining

Ámbito de especialización en el sector

Idiomas

Inglés
Bilingüe o nativo
Español
Bilingüe o nativo

Preferencias de lugar de trabajo

Solo teletrabajo

Lleva a cabo sus proyectos principalmente en remoto

AppliedAI initiative
AI Agentic Engineer
HIGH TECH
marzo de 2026 - Hoy (3 meses)
Barcelona, España
Designed RAG-based knowledge-base systems for company documentation, including source selection, metadata strategy, chunking logic, retrieval scope, and answer traceability.

Helped non-technical appliedAI users adopt AI tools by designing guided workflows, reusable Claude skills, and internal enablement patterns that reduce dependency on engineering support.

Built enterprise-safe AI workflows with scoped retrieval, source attribution, data-leakage prevention, GDPR-conscious design, and controlled tool use.

Built internal AI agent products for appliedAI teams, using real operational workflows as validation environments before adapting proven patterns for enterprise client delivery.

Supported internal AI enablement by translating technical agent capabilities into practical tools that consultants, delivery teams, and business users can apply directly in their daily workflows.
AI Agent AI Automation RAG RAG & research-agent systems (LangChain + FAISS + LLMs) n8n
Datawords
AI Engineer
HIGH TECH
agosto de 2025 - Hoy (10 meses)
Barcelona, Spain
• ● Engineered modular n8n+Python frameworks combining LLM calls, prompt templates, NLP scoring and data-visualization
pipelines to automate linguistic scoring, dataset integration and insight generation at scale.
• ● Developed dynamic query systems that turn JSON schemas into SQL plus LLM-based reasoning, exposing self-serve data
exploration and research-style insights for non-technical teams.
• ● Automated content workflows via n8n-orchestrated pipelines (Drive ,Vision,OCR,Sheets), with APIs, QC flags & reports with error-tolerant flows so processes run without manual work.
• ● Systematized brand-tone governance by using LLMs and structured rubrics to check copy against editorial, legal and UI rules,
acting as a review agent that flags issues before content ships.
• ● Ran structured A/B tests on system and user prompts across languages, improving answer relevance, reducing hallucinations and
keeping tone on-brand.
• ● Standardized prompt libraries around GPT-5/Claude-style models with logging and best-practices docs, making LLM automations traceable and reusable across teams.
Workflow automation & orchestration Python LLM prompt engineering & prompt evaluation LLM governance + reliability engineering Data engineering & self-serve analytics
Freelance
AI Prompt writer/ LLMtrainer/ AI Engineer
HIGH TECH
mayo de 2023 - Hoy (3 años y 1 mes)
Barcelona, Spain
• ● Engineered autonomous Multi-Agent Systems using LangGraph-LangChain capable of self-correction and logic-gating, transforming probabilistic LLM outputs into deterministic business actions using State Graphs.
• ● Trained and fine-tuned LLMs with custom prompt engineering and iterative feedback, boosting model accuracy by 90%.
• ● Produced AI-driven content, designing prompts & voice configs that keep long-form answers natural/consistent scaling output.
• ● Automated narration pipelines with a Dockerized FastAPI service chaining TTS, halving prep and edit time.
• ● Built a scalable AI-dubbing system with voice cloning and lip-sync to convert Spanish voices into Portuguese/English.
• ● Built LangChain + FAISS semantic-search RAG pipelines over 300+ course scripts, delivering on-brand audio/video content.
• ● Produced 100+ AI-powered content, combining AI voice clips/Synthesia avatars to lift followers by 30% and engagement by 25%.
• ● Deployed AI-powered trailers & ads speeding delivery by 5% while maintaining broadcast-quality mixes. .
• ● Ran hands-on workshops on AI-Audio processes for editors and marketers, boosting team productivity by 17%.
LLM training & fine-tuning (prompt engineering + iterative feedback loops) Multi-Agent Systems engineering (LangGraph/LangChain, state graphs, logic-gating, self-correction) RAG & semantic search (LangChain + FAISS, retrieval pipelines) AI audio production pipelines (TTS, Dockerized FastAPI, automation for narration/editing) AI localization/dubbing at scale (voice cloning + lip-sync, multilingual adaptation)