Production AI Engineer: LLMs, RAG & Scalable AI Systems

TomTom; Madrid; Presencial; EUR 90.000 - 130.000; Jornada completa

Descripción de la vacante

TomTom in Madrid, Spain is seeking an AI Engineer to design, build and run AI-powered features that reach TomTom's customers. You will turn advances in ML and LLMs into reliable production systems, collaborating with applied scientists, software engineers and product managers to move ideas from prototype to production.

You will productionize models, define evaluation frameworks, build data pipelines, and implement MLOps practices while collaborating across cross-functional teams.

Formación

• Strong programming skills in C++, Java or Python.
• Hands-on experience with LLM ecosystems, prompt engineering and RAG.
• Experience building production AI features with scalable APIs.
• Knowledge of cloud services and container orchestration.
• Familiarity with ML frameworks and MLOps practices.
• Ability to work in cross-functional teams and communicate clearly.

Responsabilidades

• Design, build and run AI-powered features and products.
• Productionize models and maintain low-latency services.
• Define evaluation benchmarks, guardrails and monitoring.
• Develop data pipelines for training, fine-tuning and inference.
• Implement CI/CD, versioning and observability for ML systems.
• Collaborate with product, engineering and science teams.
• Stay updated on AI advances and share learnings.

Conocimientos

C++

Java

Python

LLMs & prompt engineering

RAG

ML pipelines

Cloud platforms (Azure/AWS/GCP)

CI/CD

Docker

Kubernetes

Git

PyTorch/TensorFlow/Hugging Face

MLOps

Geospatial data knowledge

Educación

Bachelor's degree in CS/Engineering/AI or related field

Herramientas

Docker

Kubernetes

Git

MLflow

Experiment tracking

Vector databases

Descripción del empleo

TomTom in Madrid, Spain is seeking an AI Engineer to design, build and run AI-powered features that reach TomTom's customers. You will turn advances in ML and LLMs into reliable production systems, collaborating with applied scientists, software engineers and product managers to move ideas from prototype to production.

You will productionize models, define evaluation frameworks, build data pipelines, and implement MLOps practices while collaborating across cross-functional teams.