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Join our dynamic team and vibrant culture to contribute to shaping the future of location technology.\nRole Overview: We are looking for an AI Engineer to design, build and run AI-powered features and products that reach TomTom's customers. You'll turn advances in machine learning and large language models (LLMs) into reliable, scalable production systems. You'll work closely with applied scientists, software engineers and product managers to take ideas from prototype to production, and you'll help raise the standard for how AI is built and evaluated across TomTom.\nResponsibilities:\n 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. \nProductionize models: Take models and prototypes from applied scientists and turn them into robust, low-latency, cost-efficient services that scale to global traffic. \nEvaluation 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. \nData 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. \nMLOps and infrastructure: Implement CI/CD for ML, model versioning, experiment tracking and observability on cloud platforms. \nCross-functional collaboration: Work with product, engineering and science stakeholders to understand requirements, weigh trade-offs and deliver AI solutions that meet customer needs. \n 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. \nQualifications\nBachelor's degree in Computer Science, Engineering, AI/ML or a related field, or equivalent professional experience. \nStrong proficiency in one or more high-level programming languages such as C++, Java, Python, or similar languages. \nHands-on experience with LLMs and their ecosystem: prompt engineering, RAG, embeddings and vector databases, tool use and agent frameworks, and fine-tuning is preferred. \nSolid understanding of software architecture, API design, design patterns and best practices for maintainable, scalable systems. \nExperience with cloud service providers (e.g., Azure, AWS, GCP), containerization (Docker, Kubernetes) and CI/CD tools. \nKnowledge of version control systems, preferably Git. \nExcellent problem-solving and communication skills, with the ability to work effectively in a cross-functional team. \nExperience with ML frameworks and libraries such as PyTorch, TensorFlow or Hugging Face is preferred. \nFamiliarity with MLOps practices and tools (e.g., MLflow, experiment tracking, model monitoring) is preferred. \nExperience with geospatial, mapping or location data is a plus, but not 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