Senior AI Engineer Engineering Navigation Intelligence(Product Engineering) Amsterdam, The Netherlands

TomTom International; Amsterdam; Hybride; EUR 70 000 - 100 000; Plein temps

Résumé du poste

TomTom is seeking a Software Engineer to build a production-ready AI/data product. You will design, develop and maintain AI solutions including LLM-based apps and MLOps pipelines, spanning feature development to infrastructure and data engineering.

You will productionize models and build robust services that scale to global traffic, while defining evaluation frameworks and guardrails for accuracy, latency and cost. Join a cross-functional team and help advance TomTom's AI capabilities.

Qualifications

• Bachelor's degree or equivalent experience.
• Strong coding skills in Python/C++ and related languages.
• Hands-on experience with LLMs, RAG and embeddings.
• Knowledge of software architecture and API design.
• Experience with cloud providers, containers and CI/CD tools.
• Familiarity with Git and version control.
• Excellent problem-solving and communication in cross-functional settings.
• Experience with PyTorch, TensorFlow or Hugging Face.
• Familiarity with MLOps tools and practices.
• Experience with geospatial data is a plus.

Responsabilités

• Build production AI solutions including LLM-based apps and ML services.
• Productionize models to be low-latency and scalable.
• Define evaluation benchmarks and monitor models in production.
• Build and optimize data pipelines for training and inference.
• Implement MLOps, model versioning and observability on cloud platforms.
• Collaborate with product, engineering and science stakeholders.
• Keep up with AI ecosystem and share learnings with the team.

Connaissances

Python

C++

LLMs / NLP

Data pipelines

Cloud / CI/CD

Formation

Bachelor's degree in Computer Science or related

Description du poste

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.

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