Senior AI Engineer

TomTom; Amsterdam; Presencial; EUR 70.000 - 110.000; Jornada completa

Descripción de la vacante

TomTom is seeking a Software Engineer to help build a production-ready AI/data product. The role spans feature development, infrastructure and data engineering to deliver scalable AI solutions.

You will design and implement AI systems, optimize data pipelines, and collaborate with product, engineering and science teams to deliver value while upholding privacy and security standards.

Formación

• Experience with AI/ML systems and production-grade deployments.
• Strong programming skills in C++, Java or Python.
• Hands-on with LLMs, RAG, embeddings and vector databases.
• Familiarity with CI/CD, cloud services and observability.

Responsabilidades

• Design, develop and maintain production AI solutions, including LLM-based apps, RAG pipelines and model services.
• Productionize models into robust, low-latency services that scale globally.
• Define evaluation frameworks and guardrails for accuracy, safety, latency and cost.
• Build and optimize data pipelines for training, fine-tuning, embedding and inference.
• Implement CI/CD for ML, model versioning, experiment tracking and observability on cloud platforms.
• Collaborate with product, engineering and science stakeholders to meet customer needs.
• Stay current with AI advances and share learnings with the team.

Conocimientos

C++/Java/Python

LLMs & RAG

API design

Cloud platforms

Git

MLOps

Docker

Kubernetes

ML frameworks

Herramientas

Docker

Kubernetes

Git

Descripción del empleo

We’re looking for a Software Engineer to join our team and help us build a production-ready AI/data product for TomTom. The work is covering everything from new feature development to infrastructure, and data Engineering.

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.