JP Javier Pontón / AI Engineer CV

AI Engineer / LLM Engineer · Independent B2B contractor

Available immediately

I build AI systems that survive an audit, not just a demo.

I build AI systems for companies that cannot afford them to be approximately right: document extraction with vision-language models, retrieval over regulation and internal documentation, multi-agent workflows, and self-hosted inference for clients who cannot send their data to an API. Seven years of production backend engineering for Iberia, KPN, Mercadona, Inditex, Openbank and Idealista sit behind every one of them.

Javier Pontón González, AI and LLM engineer

Javier Pontón González

AI / LLM Engineer · Agentic Systems, RAG and On-Premise Inference

7+

Years production backend engineering

Iberia · KPN · Mercadona · Inditex · Openbank · Idealista

2+

Years shipping Generative AI to production

Agents, RAG, VLM extraction, self-hosted inference

4

Live products founded and engineered solo

Facturias · ZORRO · Apunta · Grabia

0

Gaps between engagements since 2023

Back-to-back enterprise contracts

Shipped production systems for

Seven enterprises across aviation, telecoms, retail, logistics, banking and real estate.

IberiaAviation KPNTelecommunications MercadonaRetail InditexRetail and logistics OpenbankBanking, Santander Group idealistaReal estate Empathy.coE-commerce search

Most applied AI fails in the same four places.

Nobody defined what correct looks like before building. Nobody can trace an output back to the document it came from. Nobody instrumented it so it can be debugged at three in the morning. And nobody asked whether the data was allowed to leave the building. I close all four before I write the first agent.

01

Evaluation before the build

The golden dataset and the scoring rubric exist before the first agent does. Field-level precision and recall, regression runs on every prompt or model change, shadow evaluation before anything is promoted.

02

Source-level traceability

Every extracted figure links back to its document and page. Anything the model produced without a source behind it is flagged visually and cannot pass a review gate silently.

03

Instrumented like production

OpenTelemetry traces per agent step, latency and token cost per span, failure analysis in Grafana or Dynatrace. If it cannot be debugged at three in the morning, it is not finished.

04

Runs on your hardware

Model-agnostic architecture against OpenAI-compatible APIs, vLLM and Ollama serving, GPU and VRAM sizing, quantisation. Air-gapped deployments that meet PDPL, NCA and GDPR constraints.

Eight engagements, one trace.

Enterprise production systems for Iberia, KPN, Mercadona, Inditex, Openbank, Idealista and Empathy.co, plus a Saudi systems integrator. Independent B2B contractor since November 2023, back to back.

Empathy.coJan 2019 – Mar 2021
IdealistaMar 2021 – Oct 2022
Openbank (Santander)Oct 2022 – Nov 2023
InditexNov 2023 – Mar 2024
MercadonaMar 2024 – Mar 2025
KPNMar 2025 – Feb 2026
IberiaFeb 2026 – Jul 2026
Systems integrator (KSA)Jul 2026 – Present
AI / LLM engagement Backend engineering No gaps between engagements since 2023

Jul 2026 – Present

Remote

AI / LLM

AI Engineer · IT systems integrator, Saudi Arabia (client under NDA)

Air-gapped multi-agent AI platform for public-tender (RFP) response.

  • Multi-agent AI system. Architected a two-agent solution for tender response. A financial agent performs structured extraction from vendor quotations in heterogeneous formats (PDF, Word, Excel, email, supplier portals) into a costing model; a technical agent applies RAG and Arabic OCR over RFPs of hundreds of pages, extracts scope of work and bill of quantities, and drafts the technical proposal from a fixed corporate template.
  • Human-in-the-loop and traceability. Engineered source-level traceability from every extracted figure back to its document and page, mandatory human review gates, and visual flagging of AI-generated content with no vendor source behind it, as required by tender confidentiality.
  • LLM evaluation. Defined the evaluation harness before the build: a golden dataset of historical tenders scored on field-level precision and recall, regression testing on every prompt or model change, and OpenTelemetry traces per agent step for latency, token cost and failure analysis.
  • On-premise AI infrastructure. Designed a model-agnostic architecture against OpenAI-compatible APIs, benchmarked Qwen3, DeepSeek, Falcon-H1 Arabic and ALLaM, sized GPU and VRAM for vLLM and Ollama serving, and met Saudi PDPL and NCA requirements in a fully isolated network.
  • Python
  • LangChain
  • LangGraph
  • vLLM
  • Ollama
  • Qwen3
  • DeepSeek
  • RAG
  • pgvector
  • Arabic OCR
  • OpenTelemetry
  • Docker

Feb 2026 – Jul 2026

Remote, Spain

AI / LLM

AI Engineer · Iberia, S.A.

Flight pricing platform. Technical owner of the fare pricing domain.

  • MCP server. Built and deployed an internal Model Context Protocol server exposing pricing platform tooling to LLM clients, enabling governed function calling and agentic queries over fares, providers and configuration instead of ad-hoc scripts.
  • RAG service. Delivered retrieval over internal specs and domain documentation with embeddings, hybrid search and cross-encoder reranking, cutting the time engineers spent locating pricing and NDC domain answers, instrumented with OpenTelemetry and Dynatrace like any other production service.
  • AI-assisted software engineering. Introduced Claude Code for service scaffolding, Karate and JUnit test generation and large refactors, driven by project-specific SKILL.md and CLAUDE.md conventions I defined and rolled out to the team.
  • Fare pricing services. Engineered the services that calculate flight prices by orchestrating calls to external fare-calculation providers, normalising and aggregating heterogeneous responses under IATA NDC for internal channels and distribution partners; optimised latency and cut provider calls with Redis caching on the highest-traffic pricing flows.
  • Event-driven integration. Delivered Kafka and PostgreSQL integration on AWS (ECS, SQS, S3) in hexagonal architecture with DDD, owning resilience of the pricing path (timeouts, retries and fallbacks) against third-party provider degradation.
  • Python
  • Java
  • Kotlin
  • FastAPI
  • Spring Boot
  • MCP
  • Redis
  • Kafka
  • PostgreSQL
  • AWS
  • Karate
  • Claude Code

Mar 2025 – Feb 2026

Remote, Netherlands

AI / LLM

AI Engineer · KPN

Fibre installation tracking platform, ODF International Team.

  • Generative AI in the delivery workflow. Introduced LLM-assisted development across the international team (test generation, legacy refactors, PR review) with shared prompt and review conventions, so AI output was always human-verified before merge.
  • Retrieval assistant. Built semantic search with embeddings over runbooks and ServiceNow incident history, cutting the time to find the right precedent when triaging field-operations incidents.
  • Platform delivery. Delivered a large-scale fibre installation tracking platform running in three European countries under international SLAs, building microservices and field-operations features and owning production incident resolution.
  • Data migration. Migrated historical production records from PostgreSQL to DynamoDB with chunk-based storage, offloading the operational database and keeping the hot read path fast as volume grew.
  • Python
  • Java
  • Kotlin
  • Spring Boot
  • Kafka
  • PostgreSQL
  • AWS (DynamoDB, Lambda)
  • Embeddings
  • Semantic search
  • Vue.js
  • Docker

Mar 2024 – Mar 2025

Remote, Spain

Lead Backend Engineer · Mercadona, S.A.

Product analytics platform.

  • Ingestion pipeline. Engineered a Spring Batch ingestion pipeline processing millions of product records per day from heterogeneous sources, with restartable jobs and idempotent writes.
  • API-first services. Designed API-first REST services (OpenAPI) in hexagonal architecture with DDD; sustained over 85% automated test coverage under real TDD.
  • Java
  • Kotlin
  • Spring Boot
  • PostgreSQL
  • Kafka
  • AWS
  • GCP
  • Docker
  • Flyway

Nov 2023 – Mar 2024

Remote, Spain

Lead Backend Engineer · Inditex, S.A.

Global logistics, garment-sorting event platform. Technical lead for the Azure to AWS migration.

  • High-concurrency platform. Engineered a serverless platform on Azure and AWS processing thousands of garment-sorting events per minute across the logistics network.
  • Zero-downtime migration. Led the Azure to AWS migration with zero downtime; built async REST APIs in hexagonal architecture with DDD and introduced Karate integration testing alongside the existing JUnit suite.
  • Java
  • Kotlin
  • Spring Boot
  • MongoDB
  • PostgreSQL
  • Kafka
  • AWS
  • Azure
  • Docker
  • Liquibase

Oct 2022 – Nov 2023

Madrid, Spain

Backend Engineer · Open Bank, S.A. (Santander Group)

Investment automation platform. Technical lead for the Java to Kotlin migration.

  • Serverless batch at scale. Implemented serverless batch pipelines on AWS Lambda processing millions of financial transactions per day on top of the T24 core banking system, inside a 100+ backend engineer organisation.
  • Java to Kotlin migration. Led the migration across services and Lambdas; delivered internal tech talks on DDD and hexagonal architecture to around 50 engineers.
  • Java
  • Kotlin
  • Spring Boot
  • PostgreSQL
  • AWS (Lambda, S3)
  • Elasticsearch
  • Docker
  • MockK
  • T24

Mar 2021 – Oct 2022

Madrid, Spain

Backend Engineer · Idealista, S.A.

Digital contract-signing platform.

  • Contract-signing platform. Built a digital contract-signing platform for Spain, Portugal and Italy: REST APIs plus Kafka event streams for real-time signing, with event sourcing keeping an auditable, replayable history of every contract.
  • Java
  • Kotlin
  • Spring Boot
  • PostgreSQL
  • Elasticsearch
  • Kafka
  • Docker
  • Event sourcing
  • DDD
  • TDD

Jan 2019 – Mar 2021

Gijón, Spain

Backend Engineer · Empathy.co

Playboard, e-commerce search configuration platform.

  • Search configuration platform. Built the configuration platform enterprise clients (Kroger, Carrefour, Inditex) use to tune their Elasticsearch-backed search engines; executed zero-downtime migrations: Java 8 to 11, GCP to AWS, monolith to API Gateway.
  • Java
  • Spring Boot
  • MongoDB
  • Elasticsearch
  • Docker
  • Kubernetes
  • GCP
  • AWS
  • JUnit

Four live products. Founder and sole engineer on all of them.

Not side projects with a landing page. Real users, real payments, real App Store review, real GDPR obligations — and the same evaluation and traceability discipline I bring to client work.

Facturias app icon

Facturias

Multimodal invoice-processing SaaS

Live WebiOSAndroid

Structured extraction from invoices with a self-hosted vision-language model (Qwen2.5-VL) plus RAG over Spanish tax regulation to classify and validate entries, with confidence scoring, a labelled evaluation set tracking field-level precision and recall across model versions, and human review on low-confidence fields. VeriFactu compliant, multi-tenant FastAPI backend on PostgreSQL 17 with row-level security and pgvector.

  • Vision-language extraction. Self-hosted Qwen2.5-VL reads invoices as documents rather than as OCR text dumps, preserving table structure, line items and stamps that flat OCR loses.
  • RAG over Spanish tax regulation. Retrieval over the Spanish tax code classifies and validates each entry, so a deduction is justified by a retrievable rule rather than by model intuition.
  • Confidence and human review. Every field carries a confidence score; anything under threshold is routed to human review instead of being written silently.
  • Python
  • FastAPI
  • SQLAlchemy (async)
  • PostgreSQL 17
  • Row-level security
  • pgvector
  • Qwen2.5-VL
  • Docker
ZORRO app icon

ZORRO

Consumer dating app for the gay and queer community, iOS and Android

Live iOSAndroid

Shipped solo to Google Play and the App Store in June 2026. Semantic matchmaking with embeddings and pgvector plus cross-encoder reranking, and self-hosted vision-language model moderation of user photos handling GDPR special-category data with no third-party processors. Polyglot production backend in Kotlin/Spring Boot, Elixir and Python.

  • Semantic matchmaking. Profiles are embedded and retrieved with pgvector, then reordered by a cross-encoder reranker: the same retrieve-then-rerank architecture as a production RAG pipeline, applied to people instead of documents.
  • Self-hosted photo moderation. A vision-language model screens user photos on my own infrastructure. Sexual orientation data is GDPR special-category data, so it never reaches a third-party processor.
  • Polyglot backend. Kotlin and Spring Boot for the domain, Elixir for realtime chat and presence, Python for the AI services.
  • Kotlin
  • Spring Boot
  • Elixir
  • Python
  • PostgreSQL
  • pgvector
  • Cross-encoder reranking
  • AWS SES
Apunta app icon

Apunta

Multi-tenant SaaS for shooting clubs, from custom PCB to mobile app

Live iOSAndroidWebHardware

Multi-tenant SaaS for shooting clubs owned end to end: custom NFC PCB (ESP32-C6, PN532, PoE) and C/ESP-IDF firmware, Spring Boot backend and React Native app. Range access, training sessions, scoring and club administration on one stack.

  • Custom hardware. Designed the NFC access PCB around an ESP32-C6 and a PN532 reader with Power over Ethernet, and wrote the firmware in C on ESP-IDF.
  • Backend and multi-tenancy. Spring Boot services with per-club tenancy, membership, range booking, training sessions and scoring.
  • Mobile. React Native app shipped to the App Store and Google Play for shooters and club administrators.
  • Kotlin
  • Spring Boot
  • React Native
  • PostgreSQL
  • C
  • ESP-IDF
  • ESP32-C6
  • PN532

Also

Grabia

Local pipeline with WhisperX transcription and pyannote diarisation feeding a LangChain and LangGraph agent that produces structured summaries, decisions and action items, with RAG over the meeting archive. Served from a private node (Ryzen AI MAX+ 395, 128 GB unified memory, ROCm) running Qwen3-30B under Ollama and mistral.rs; no audio or transcript leaves the machine.

  • Python
  • LangChain
  • LangGraph
  • WhisperX
  • pyannote
  • Ollama

Also

icekar

Distributed scraping and Elasticsearch search over 100,000+ car listings nightly, with an agentic LLM layer that rewrites scraper extraction rules when target sites change markup.

  • Python
  • Elasticsearch
  • LLM agents
  • Docker

What I actually work with.

Every term below is something I have shipped to production or operated, not something I have read about.

AI & LLM16 items

  • Large Language Models (LLMs)
  • Generative AI
  • Applied AI
  • Machine Learning
  • Deep Learning
  • Transformers
  • Vision-Language Models (VLMs)
  • NLP
  • Computer Vision
  • Prompt Engineering
  • Structured Outputs
  • JSON Schema
  • Function Calling
  • Tool Use
  • Hallucination Mitigation
  • Grounding

AI Agents & RAG15 items

  • Retrieval-Augmented Generation (RAG)
  • AI Agents
  • Agentic AI
  • Agentic Workflows
  • Multi-Agent Systems
  • LangChain
  • LangGraph
  • Model Context Protocol (MCP)
  • Embeddings
  • Semantic Search
  • Hybrid Search
  • Cross-Encoder Reranking
  • Vector Databases
  • pgvector
  • HNSW

LLM Evaluation & LLMOps22 items

  • LLM Evaluation
  • Golden Datasets
  • Regression Testing
  • Precision
  • Recall
  • F1
  • LLM-as-a-Judge
  • Human Evaluation
  • RAG Evaluation
  • Hit Rate
  • MRR
  • A/B Testing
  • Shadow Evaluation
  • RAGAS
  • Promptfoo
  • LLMOps
  • OpenTelemetry
  • Prometheus
  • Grafana
  • Dynatrace
  • Cost Optimization
  • Latency Optimization

Model Serving & AI Infrastructure20 items

  • vLLM
  • Ollama
  • mistral.rs
  • OpenAI-Compatible APIs
  • PyTorch
  • HuggingFace
  • Quantization
  • GGUF
  • AWQ
  • LoRA
  • QLoRA
  • GPU/VRAM Optimization
  • Self-Hosted Inference
  • On-Premise AI
  • Air-Gapped Deployment
  • Qwen3
  • Qwen2.5-VL
  • DeepSeek
  • Claude API
  • OpenAI API

Backend & Data20 items

  • Python
  • Java
  • Kotlin
  • Elixir
  • TypeScript
  • SQL
  • Spring Boot
  • FastAPI
  • Spring Batch
  • REST
  • OpenAPI
  • Microservices
  • PostgreSQL
  • pgvector
  • MongoDB
  • Elasticsearch
  • Redis
  • DynamoDB
  • Apache Kafka
  • AWS SQS

Cloud & DevOps9 items

  • AWS
  • Azure
  • GCP
  • Docker
  • Kubernetes
  • GitHub Actions
  • Jenkins
  • Git
  • CI/CD

Architecture & Engineering9 items

  • Hexagonal Architecture
  • Domain-Driven Design (DDD)
  • CQRS
  • Event Sourcing
  • SOLID
  • TDD
  • API-First
  • Technical Leadership
  • Mentoring

How companies bring me in.

From a two-week assessment that tells you honestly whether an LLM is the right tool, to owning a production AI system end to end.

01

AI feasibility assessment

A two-week engagement: I map the workflow, define what accuracy would have to mean for it to be trusted, and tell you honestly whether an LLM is the right tool. Sometimes the answer is that it is not, and that is worth knowing before anyone signs a platform contract.

02

RAG and retrieval systems

Embeddings, hybrid search, cross-encoder reranking, chunking strategy and a retrieval evaluation harness with hit rate and MRR.

03

AI agents and multi-agent workflows

LangChain, LangGraph, MCP servers, function calling and tool use, with traces per step and human review gates where they matter.

04

Document extraction with vision-language models

Structured outputs from PDFs, scans, spreadsheets and email, with confidence scoring, field-level precision and recall, and source-level traceability.

05

Self-hosted and air-gapped inference

vLLM and Ollama serving, model benchmarking and selection, quantisation, GPU and VRAM sizing, deployments that never leave your network.

06

LLM evaluation and LLMOps

Golden datasets, regression suites, LLM-as-a-judge, shadow evaluation, OpenTelemetry instrumentation, cost and latency budgets.

The questions recruiters and clients actually ask.

Answered in full here so a person, a search engine or an AI assistant can all get the same answer without emailing first.

What does Javier Pontón do?

Javier Pontón González is an AI Engineer and LLM Engineer based in Asturias, Spain, working fully remotely for clients in the EU, UK and US. He designs and operates production LLM systems: AI agents and multi-agent workflows with LangChain, LangGraph and MCP, Retrieval-Augmented Generation with embeddings and cross-encoder reranking, structured document extraction with vision-language models, and self-hosted or air-gapped inference. He has 7+ years of production backend engineering behind that, for Iberia, KPN, Mercadona, Inditex, Openbank and Idealista.

Is Javier Pontón available for hire or contract work?

Yes. He works as an independent B2B contractor invoiced from Spain, is available immediately, and works fully remotely across EU, UK and US time zones. He takes both long-running engagements and shorter scoped work such as a two-week AI feasibility assessment. He can be reached at javierpontongonzalez@gmail.com or +34 623 920 307.

What is Javier's experience with RAG and AI agents?

He built a production Model Context Protocol server and a RAG service over engineering documentation at Iberia, using embeddings, hybrid search and cross-encoder reranking, instrumented with OpenTelemetry and Dynatrace. He architected a two-agent system for Arabic public-tender response for a Saudi systems integrator, combining structured extraction from vendor quotations with RAG and Arabic OCR over RFPs of hundreds of pages. He also ships RAG in his own products: Facturias retrieves over Spanish tax regulation, and Grabia retrieves over a local meeting archive.

Does Javier work with on-premise or air-gapped LLM deployments?

Yes, this is a core specialisation. He designed and delivered a fully air-gapped multi-agent platform for a Saudi systems integrator under PDPL and NCA requirements: model-agnostic architecture against OpenAI-compatible APIs, benchmarking of Qwen3, DeepSeek, Falcon-H1 Arabic and ALLaM, GPU and VRAM sizing for vLLM and Ollama serving, and no data leaving the client network. His own products run self-hosted vision-language models for the same reason, including GDPR special-category data in ZORRO.

How does Javier evaluate LLM systems?

Evaluation is defined before the build. A golden dataset representing real inputs, field-level precision and recall as the scoring rubric, regression runs on every prompt or model change, LLM-as-a-judge and human evaluation where the output is subjective, and hit rate and MRR for the retrieval layer. Everything is instrumented with OpenTelemetry so latency, token cost and failure modes are visible per agent step.

What products has Javier built and shipped?

Four live products as founder and sole engineer. Facturias (facturias.es) is a multimodal invoice-processing SaaS with a self-hosted vision-language model and RAG over Spanish tax regulation. ZORRO (somoszorro.com) is a dating app for the gay and queer community on iOS and Android with semantic matchmaking and self-hosted photo moderation. Apunta (apuntapp.com) is a multi-tenant SaaS for shooting clubs owned end to end from custom NFC hardware to the mobile app. Grabia is a fully local meeting-intelligence pipeline running on a private inference node.

What is Javier's technical stack?

Python, Java, Kotlin and Elixir on the language side. FastAPI and Spring Boot for services. LangChain, LangGraph and MCP for agents. PostgreSQL with pgvector, Elasticsearch, Redis, DynamoDB and MongoDB for data. Kafka for events. vLLM, Ollama and mistral.rs for self-hosted inference. AWS, Azure and GCP with Docker and Kubernetes. Hexagonal architecture, DDD and TDD as the default way of building.

Where is Javier located and which languages does he speak?

He is based in Asturias, Spain, in the Europe/Madrid time zone, and works fully remotely with clients across the EU, UK and US. He is a native Spanish speaker with professional working proficiency in English.

07 / Contact

If a document-heavy process is costing you real money, the first conversation is free.

Available immediately. Independent B2B contractor, invoiced from Spain. Fully remote (EU, UK, US). I answer every message personally, usually within a day.