Juan Carlos Jiménez

Juan Carlos Jiménez, AI Engineer, Madrid

I see INCIDENTS: an LLM that flags incidents in real customer cases.

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Work

Eleven projects. Six in production at Telefónica, two theses and three of my own.

  1. 01Incident detectionAn LLM pipeline that reads real customer cases and raises incidents before they spread.Telefónica
    2024
  2. 02Generative chatbotThe customer-care chatbot, moved from intent prediction to generative answers without losing control.Telefónica
    2024
  3. 03Survey classifierA Spanish BERT model, fine-tuned to classify customer surveys and to know when to trust itself.Telefónica
    2025
  4. 04Review opsAn internal LLM tool that drafts and manages replies to App Store and Google Play reviews.Telefónica
    2024
  5. 05Ops automationsSmall services that put LLMs inside operational processes: churn reasons, case analysis, survey data.Telefónica
    2023–
  6. 06Self-service portalThe digital self-service portal for customer care, from first proposal to rollout.Telefónica
    2023
  7. 07CargoCompare agentMaster's thesis: retrieval and a ReAct agent with deterministic tools that answer freight-rate questions in CargoCompare, a quoting platform for hauliers.Thesis
    2026–27
    In progress
  8. 08JetsamKnow what makes it through airport security before you pack. A ruling with cited sources, resolved across airline, airport, regulator and date.Personal
    2026
    Under construction
  9. 09Bargain AgentAsk for a weekend away from Madrid on a budget and get ranked destinations with weather, deals and hotel prices, each price with its link and none invented.Personal
    2026
  10. 10Meeting CopilotA native macOS app that transcribes meetings live and answers questions about them, running locally by default.Personal
    2026
    MVP
  11. 11Perception for autonomous drivingA visual recognition system for autonomous driving: pedestrians, distance and lane markings in real time, in bad weather.Thesis
    2024

Generative AI, past the demo.

I work where generative AI stops being a demo and enters a real process, with real users, real latency, real cost and real adoption.

The code is half the job. The other half is picking the process worth automating, measuring it, and getting the team to actually use it.

  • Every answer cites its source.
  • Code prints the numbers. The model chooses.
  • Every change is measured before it ships.

Path

I'm Juan Carlos Jiménez, an AI Engineer at Telefónica / Movistar Plus in Madrid, Spain. For three years I have designed, shipped and run generative AI in production: LLM systems, RAG, fine-tuning, agents and their evaluation, on top of a background in computer vision. I hold a BSc in Computer Engineering from Universidad Europea and I am studying an MSc in Artificial Intelligence at UNIR.

Experience

AI EngineerOct 2023 – now

Telefónica / Movistar Plus · Tres Cantos, Madrid

LLM solutions for digital operations and customer care: incident detection, a generative chatbot, fine-tuned NLP, review automation and a self-service portal. Also the link between business, operations and development, prioritising what to automate.

Education

MSc in Artificial Intelligence2026 – 2027, in progress

UNIR

BSc in Computer Engineering2020 – 2024

Universidad Europea

Certifications

Building AI Agents with MongoDBSep 2026

MongoDB

Building RAG Apps Using MongoDBSep 2026

MongoDB

Stack

LLMs
Azure OpenAI · OpenAI API · Fine-tuning · RAG · Embeddings · Guardrails · MCP · LLM evaluation
Agents
ReAct · LangGraph · Tool calling · Human in the loop
Vision and ML
YOLO · CNN · OpenCV · CUDA · scikit-learn · pandas
Search and data
Azure AI Search · Vector search · BM25 · Qdrant · SQL · NoSQL
Cloud and backend
Azure Functions · FastAPI · REST APIs · Docker · GitHub Actions · Azure DevOps · Bicep
Languages
Python · SQL · TypeScript · Swift · Java

Say hello.

Open to conversations about LLM systems that have to work outside a demo.

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