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Javier CuadraJC

Javier Cuadra

GenAI Tech Lead | LLM Pipelines & Agentic Engineer

600 €/día
Marbella, ES
8-15 años

Tiempo medio de respuesta: 1h

Acerca de Javier

I help companies build production-ready GenAI systems — not proofs of concept, not demos, but pipelines that run at scale and deliver real business value.

As GenAI Tech Lead at Zurich Insurance, I architect LLM-powered systems that process 10,000+ documents monthly, extracting hundreds of structured variables per document. I lead teams of up to 10 engineers and own the full lifecycle: from architecture design to deployment and monitoring.

What sets me apart: I've shipped agentic AI pipelines in production using LangChain, LangGraph, LangFuse, and RAG — not just experimented with them. My background spans Amazon (predictive analytics across 50+ EU fulfillment centers), a CTO role at an AI startup, and 7+ years bridging ML research with production engineering.

What I deliver:
→ LLM-powered extraction and classification pipelines
→ Agentic AI systems (multi-agent orchestration with LangGraph)
→ RAG architectures with vector databases
→ Cloud infrastructure design (AWS, GCP)
→ MLOps/LLMOps: CI/CD, monitoring with LangFuse, Terraform IaC
→ Technical leadership and team mentoring

Stack: Python, LangChain, LangGraph, LangFuse, RAG, PyTorch, FastAPI, Pydantic, Docker, Terraform, AWS, GCP, MongoDB, SQL.

Previously: Amazon, Zurich Insurance, CTO at Nitcai. Aerospace Engineer (UPM) + Master in Big Data.
  • Español

    Bilingüe o nativo

  • Inglés

    Competencia profesional completa

Acepta trabajo presencial
Marbella (hasta 50 km)

Experiencia

  • Zurich Insurance
    GenAI Tech Lead
    HIGH TECH
    septiembre de 2024 - Hoy (1 año y 11 meses)
    Spain
    Lead a team of up to 10 engineers across two document understanding projects using LLMs. Own technical strategy, architecture, and delivery for business-critical data extraction systems.
    Policy Document Intelligence
    – Designed and deployed LLM extraction pipeline processing 5,000–10,000 policy documents/month, extracting 200–500 structured variables per document
    – Replaced legacy manual systems for detecting discrepancies between master and local policies, eliminating coverage misalignment that previously required full manual review
    – Transformed unstructured policy data into structured KPI feeds for business analytics and compliance monitoring Clause Detection & Language Quality
    – Built system to identify specific legal clauses and extract relevant text snippets from policy documents
    – Developed quality scoring framework assessing language clarity and regulatory compliance
    Technical leadership product development
    LLM Langchain Python RAG LangGraph
  • LiveWell by Zurich — Zurich Insurance
    Tech Lead
    HIGH TECH
    mayo de 2022 - enero de 2024 (1 año y 8 meses)
    Spain
    Promoted from Senior DS to Tech Lead. Led teams of 3–5 building AI-powered wellbeing features for a consumer health platform serving thousands of monthly active users.
    – Designed and shipped Generative AI content feature delivering personalized wellbeing recommendations
    – Built NLP validation layer (TF-IDF, embeddings) ensuring LLM output quality before user-facing delivery
    – Built end-to-end recommendation engine using BERT embeddings for personalized content delivery
    – Designed DynamoDB schema and serverless API (AppSync + Lambda) powering the recommendation layer
    – Architected data model on DynamoDB + Redshift Serverless; defined all infrastructure as Terraform IaC with CI/CD on GitHub Actions
    LLM Python RAG LangGraph Langchain
  • Amazon
    Data Scientist II — EU RME Predictive Analytics
    junio de 2020 - agosto de 2021 (1 año y 2 meses)
    Remote
    Built predictive maintenance and NLP systems for Amazon's Reliability & Maintenance Engineering team, analyzing data across 50–60 European fulfillment centers.
    – Designed predictive maintenance models (XGBoost) for industrial machinery to reduce unplanned downtime across EU operations
    – Built NLP system to detect temporal patterns in maintenance work orders, improving scheduling efficiency
    – Conducted exploratory analysis on terabyte-scale datasets spanning the majority of Amazon's EU fulfillment network
    – Developed NLP tools for automated data quality audits of maintenance work orders

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Formación

  • Master — Big Data and Analytics
    MBIT School
    2019
    Master — Big Data and Analytics
  • Bachelor of Aerospace Engineering
    Universidad Politécnica de Madrid (UPM)
    2017
    Bachelor of Aerospace Engineering

Conjunto de habilidades profesionales

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