We are looking for a highly motivated Machine Learning (ML) Engineer to join our growing AI Center of Excellence, responsible for using machine learning to drive innovation across the organization. We welcome applications from Spain (office: Barcelona) with an option of remote work. Join us!
Your global responsibilities will include (but will not be limited to):
Leverage data to solve business problems of various business units at EcoVadisDesign, develop and maintain scalable ML systemsEnsure ML lifecycle monitoring by applying the best practices in MLOps and green ITEngage in building ML engineering infrastructure and systems to orchestrate batch and streaming pipelines, and leverage Infrastructure as Code (IaC) to manage and provision infrastructurePartner with scientists and engineers to make ML models accessible to end-users and downstream processesBuild and enhance feature storesLeverage Python, MLflow, Azure stack (e.g., Azure cloud, Azure ML, Azure Data Factory) and Databricks to deliver end-to-end solutionsYou have an outgoing personality along with an exceptional level of drive and a desire to pursue a career in an international and dynamic environment. You also possess excellent verbal and written communication, critical thinking and analytical skills.
M.S. Degree in Computer Science, Mathematics, Engineering, or a related technical disciplineDemonstrable experience in designing ML systems and integrating those into business applicationsExcellent knowledge of Python, other languages are a plusDeep knowledge of machine learning lifecycle, principles, and toolingExperience in cloud technology, preferably Azure and its ecosystem (e.g., Azure Data Factory, Azure Bicep)Solid understanding of software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operationsExperience in Linux OS and scriptingKnowledge of data management fundamentals and data storage principlesExperience of cross-functional collaboration in the development of ML products and services (e.g., Engineers, Product Managers) is a plusFamiliarity with (non) relational databases
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