Senior AI Engineer

TomTom; Amsterdam; On-site; EUR 90,000 - 135,000; Full time

Job summary

TomTom is seeking a Software Engineer to help build a production-ready AI/data product. You will work on features, infrastructure and data engineering for AI systems, including LLM-based applications and RAG pipelines.

You will productionize models, design robust architectures, and implement CI/CD on cloud platforms while collaborating with product and science teams to deliver value at scale.

Qualifications

• Bachelor's degree in CS/engineering/AI or related field or equivalent experience.
• Proficiency in C++, Java, or Python.
• Hands-on experience with LLMs, RAG, embeddings and vector databases.
• Strong understanding of scalable software architecture and API design.
• Experience with cloud providers (Azure/AWS/GCP) and CI/CD.
• Familiarity with ML frameworks (PyTorch, TensorFlow, Hugging Face).

Responsibilities

• Design, develop and maintain production AI solutions, including LLM-based apps and RAG pipelines.
• Productionize models into scalable services with low latency and cost efficiency.
• 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 and observability on cloud platforms.
• Collaborate with product, engineering and science stakeholders to meet customer needs.
• Keep up with AI advances and share learnings with the team.

Skills

C++/Java/Python

LLMs & RAG

Software architecture

CI/CD

Git

Communication

Cloud platforms

Education

Bachelor's degree in CS/AI or related

Tools

Docker

Kubernetes

Git

PyTorch

Job description

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.