AI Engineer — Production ML & LLM Systems

TomTom; Madrid; Híbrido; EUR 60.000 - 90.000; Jornada completa

Descripción de la vacante

TomTom is seeking an AI Engineer for an on-site role in Madrid to design, build, and operate AI-powered features for location technology products. You will turn advances in ML and LLMs into reliable production systems, collaborating with engineers, scientists and product managers to take ideas from prototype to production.

The role emphasizes building robust, scalable AI services, optimizing data pipelines, and implementing MLOps, with a focus on safety, latency, and cost efficiency in a

Formación

• Bachelor's degree in CS/Engineering/AI or equivalent.
• Hands-on experience with LLMs, prompt engineering, RAG, embeddings and vector databases.
• Experience with ML frameworks PyTorch or TensorFlow and Hugging Face.
• Familiarity with cloud services, CI/CD tooling and containerization (Docker/Kubernetes).

Responsabilidades

• Design, build and run AI-powered features and products for TomTom customers.
• Productionize models into robust, low-latency services that scale globally.
• Define evaluation frameworks for accuracy, safety, latency and cost; monitor models in production.
• Build and optimize data pipelines for training, fine-tuning, embedding and inference.
• Implement MLOps practices: CI/CD, model versioning and observability on cloud platforms.
• Collaborate with product, engineering and science stakeholders to meet customer needs.
• Stay up-to-date with the AI ecosystem and share learnings with the team.

Conocimientos

C++

Java

Python

LLMs

RAG

Embeddings

Git

Docker

Kubernetes

Cloud platforms

Educación

Bachelor's degree in CS/Engineering/AI or equivalent

Herramientas

PyTorch

TensorFlow

Hugging Face

MLflow

Descripción del empleo

TomTom is seeking an AI Engineer for an on-site role in Madrid to design, build, and operate AI-powered features for location technology products. You will turn advances in ML and LLMs into reliable production systems, collaborating with engineers, scientists and product managers to take ideas from prototype to production.

The role emphasizes building robust, scalable AI services, optimizing data pipelines, and implementing MLOps, with a focus on safety, latency, and cost efficiency in a