AI Engineer

Lever, Inc.; Madrid; Presencial; EUR 70.000 - 95.000; Jornada completa

Descripción de la vacante

TomTom is seeking an AI Engineer to design, build and run AI-powered features that reach customers.

You will turn advances in machine learning and LLMs into reliable, scalable production systems, collaborating with scientists, software engineers and product managers to move ideas from prototype to production.

Role emphasizes CI/CD, model versioning, experiment tracking and observability across cloud platforms, with a fast-moving AI ecosystem and strong cross-functional collaboration.

Formación

• Bachelor's degree in CS/engineering/AI or equivalent experience.
• Strong programming skills in C++, Python and related ecosystems.
• Hands-on experience with LLMs, RAG, embeddings and tool use.
• Knowledge of scalable, API-driven architectures and best practices.

Responsabilidades

• Build production AI solutions, including LLM-based applications and RAG workflows.
• Productionize models into low-latency, scalable services.
• Define evaluation benchmarks, guardrails and monitoring for ML systems.
• Develop and optimize data pipelines for training and inference.
• Implement CI/CD, model versioning and observability on cloud platforms.
• Collaborate with product, engineering and science teams to deliver AI features.

Conocimientos

C++

Python

LLMs

RAG

Cloud platforms

Docker

Kubernetes

Git

PyTorch

TensorFlow

Hugging Face

Educación

Bachelor's degree in CS/Engineering/AI

Herramientas

Docker

Kubernetes

Git

PyTorch

TensorFlow

Hugging Face

MLflow

Descripción del empleo

About TomTom:TomTom is a global leader in navigation, mapping, and traffic information. Join our dynamic team and vibrant culture to contribute to shaping the future of location technology.
Role Overview:We are looking for an AI Engineer to design, build and run AI-powered features and products that reach TomTom's customers. You'll turn advances in machine learning and large language models (LLMs) into reliable, scalable production systems. You'll work closely with applied scientists, software engineers and product managers to take ideas from prototype to production, and you'll help raise the standard for how AI is built and evaluated across TomTom.

Responsibilities:

• Build AI systems:Design, develop and maintain production AI solutions, including LLM-based applications, retrieval-augmented generation (RAG) pipelines, agentic workflows and ML model services.
• Productionize models:Take models and prototypes from applied scientists and turn them into robust, low-latency, cost-efficient services that scale to global traffic.
• Evaluation and quality:Define and automate evaluation frameworks, benchmarks and guardrails that measure accuracy, safety, latency and cost, and monitor models once they're in production.
• Data and pipelines:Build and optimize data pipelines for training, fine-tuning, embedding and inference, making sure data is high quality, traceable and handled in line with privacy requirements.
• MLOps and infrastructure:Implement CI/CD for ML, model versioning, experiment tracking and observability on cloud platforms.
• Cross-functional collaboration:Work with product, engineering and science stakeholders to understand requirements, weigh trade-offs and deliver AI solutions that meet customer needs.
• Continuous improvement:Keep up with the fast-moving AI ecosystem, evaluate new models, tools and techniques, and share what you learn with the wider engineering community.

Qualifications

• Bachelor's degree in Computer Science, Engineering, AI/ML or a related field, or equivalent professional experience.
• Strong proficiency in one or more high-level programming languages such as C++, Java, Python, or similar languages.
• Hands-on experience with LLMs and their ecosystem: prompt engineering, RAG, embeddings and vector databases, tool use and agent frameworks, and fine-tuning is preferred.
• Solid understanding of software architecture, API design, design patterns and best practices for maintainable, scalable systems.
• Experience with cloud service providers (e.g., Azure, AWS, GCP), containerization (Docker, Kubernetes) and CI/CD tools.
• Knowledge of version control systems, preferably Git.
• Excellent problem-solving and communication skills, with the ability to work effectively in a cross-functional team.
• Experience with ML frameworks and libraries such as PyTorch, TensorFlow or Hugging Face is preferred.
• Familiarity with MLOps practices and tools (e.g., MLflow, experiment tracking, model monitoring) is preferred.
• Experience with geospatial, mapping or location data is a plus, but not required.