Senior AI Engineer

TomTom

. Design, develop and maintain production AI solutions, including LLM-based applications, RAG pipelines, agentic workflows and ML model services .

Posted 11/3/2026full-timeAmsterdam • NetherlandsSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and developing AI solutions, particularly with LLMs, while ensuring high-quality data management and compliance with privacy requirements. Proficient in implementing CI/CD practices for machine learning and collaborating effectively with cross-functional teams to deliver scalable services.

Highest-signal resume keywords

LLM-Based ApplicationsCI/CD Implementation for MLCloud Service Providers (Azure, AWS, GCP)High-Level Programming Languages (C++, Java, Python)ML Frameworks (PyTorch, TensorFlow, Hugging Face)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData Pipeline OptimizationSoftware ArchitectureAPI DesignPrompt EngineeringFine-TuningVersion Control (Git)Model MonitoringExperiment TrackingCost-Efficient Services

Soft Skills

Problem-SolvingCommunicationCross-Functional Collaboration

Tools & Technologies

DockerKubernetesMLflowVector DatabasesAgent Frameworks

Industry Keywords

MLOpsGeospatial DataMapping DataPrivacy Requirements

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoogle Cloud PlatformJavaKubernetesPythonPyTorchTensorflowC++

About the role

Key responsibilities & impact

Design, develop and maintain production AI solutions, including LLM-based applications, RAG pipelines, agentic workflows and ML model services
Take models and prototypes from applied scientists and turn them into robust, low-latency, cost-efficient services that scale to global traffic
Define and automate evaluation frameworks, benchmarks and guardrails measuring accuracy, safety, latency and cost
Monitor models once they are in production
Build and optimize data pipelines for training, fine-tuning, embedding and inference
Ensure data is high quality, traceable and handled in line with privacy requirements
Implement CI/CD for ML, model versioning, experiment tracking and observability on cloud platforms
Work with product, engineering and science stakeholders to understand requirements, weigh trade-offs and deliver AI solutions meeting customer needs
Evaluate new models, tools and techniques and share learnings with the wider engineering community

Requirements

What you’ll need

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; 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 such as Azure, AWS, or GCP; containerization with Docker and 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 such as MLflow, experiment tracking and model monitoring is preferred
Experience with geospatial, mapping or location data is a plus, but not required