Senior AI Engineer

Carbon Data Solutions; Amsterdam; Hybrid; EUR 90,000 - 130,000; Full time

Job summary

TomTom is seeking a Senior AI Engineer to help build a production-ready AI/data product. You will develop AI systems, MLOps pipelines, and scalable data services in collaboration with product and engineering teams in Amsterdam.

Ideal candidates have strong programming skills (C++, Java, Python) and hands-on experience with LLMs, RAG, embeddings, and cloud-based ML infrastructure. The role emphasizes scalable design and cross-functional teamwork.

Qualifications

• Bachelor's degree in Computer Science, Engineering, AI/ML or a related field, or equivalent professional experience.
• Strong proficiency in high-level programming languages such as C++, Java, Python, or similar.
• 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 (Azure, AWS, GCP), containerization (Docker, Kubernetes) and CI/CD tools.
• Knowledge of version control systems, preferably Git.
• Excellent problem-solving and communication skills, in cross-functional teams.
• Experience with ML frameworks (PyTorch, TensorFlow, Hugging Face) preferred.

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: Turn models and prototypes into robust, low-latency services that scale to global traffic.
• Define and automate evaluation frameworks, benchmarks and guardrails for accuracy, safety, latency and cost.
• Build and optimize data pipelines for training, fine-tuning, embedding and inference, ensuring data quality and privacy.
• Implement MLOps and infrastructure: 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 ecosystem, evaluate new models/tools and share learnings with the team.

Skills

Programming languages (C++, Java, Py)

LLMs ecosystem

Software architecture & API design

Cloud services (Azure, AWS, GCP)

CI/CD tooling

Git version control

Cross-functional collaboration

Education

Bachelor's degree in Computer Science / Engineering / AI or related field

Tools

Docker

Kubernetes

Git

CI/CD tooling

Job description

Senior AI Engineer at Tomtom, via carbon.talent

We’re looking for a Software Engineer to join our team and help us build a production-ready AI/data product for TomTom.

Amsterdam, The Netherlands

Stack

Must-haves

• 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.

About this role

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.