At TomTom, we are seeking a highly motivated Software Engineer to join our MLOps team. As part of this team, you will be at the forefront of transforming our AI/ML capabilities throughout the company, paving the way for other teams to revolutionize the world of mapmaking with innovative, scalable and cost-efficient solutions.
What you'll do: Lead the creation, maintenance, and optimization of cloud-based infrastructure that enables massive data storage and processing for different ML model types. Collaborate with a global team of engineers to ensure the infrastructure is scalable, highly available, and resilient, meeting the demands of real-time data processing. Design and implement technical solutions that address complex challenges, particularly in data processing and infrastructure globally across the company. Play a pivotal role in integrating Machine Learning Operations (MLOps) tools and frameworks where necessary, working with technologies like Kubernetes and cloud infrastructure. Partake in design decisions, contribute to architectural discussions, and take ownership of major projects or project steps, ensuring high-quality solutions are delivered. Troubleshoot and resolve complex technical issues, leveraging your deep understanding of infrastructure design and cloud technologies. What you'll need: A Bachelor's degree in Computer Science (or related field) or 4+ years of relevant industry experience in infrastructure development. Expertise in building and managing data processing systems with cloud technologies such as Azure, Kubernetes, and Databricks. Proficiency in at least one modern programming language, such as Python or Scala, and experience with scalable system design. Solid understanding of professional software engineering best practices—code reviews, source control, testing, and deployment processes. Ability to work independently, taking ownership of projects and delivering high-quality, maintainable solutions. Excellent communication skills, both written and verbal, in English, to collaborate effectively with a distributed team. Nice to have: Experience in Machine Learning, algorithms, and Computer Vision technologies, though these are considered a bonus. Strong familiarity with MLOps principles and tools, including CI/CD pipelines, observability, and infrastructure as code (IaC). What we offer: A competitive compensation package, of course. Time and resources to grow and develop, including a personal development budget and paid leave for learning days, as well as paid access to e-learning resources such as O'Reilly and LinkedIn Learning. Time to support life outside of work, with enhanced parental leave plus paid leave to care for loved ones and volunteer in local communities. Work flexibility, where TomTom'ers, in agreement with their manager and team, use both the office and home to focus, collaborate, learn and socialize.
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