Descripción de la oferta
About Tom Tom Tom Tom is a global leader in navigation, mapping, and traffic information. Join our dynamic team and vibrant culture to contribute to shaping the future of location technology. Role Overview We are looking for an AI Engineer to design, build and run AI-powered features and products that reach Tom Tom'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 Tom Tom. 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 Py Torch, Tensor Flow 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. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. #J-18808-Ljbffr
