Full-Time Senior Machine Learning Engineer (MLOps)

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 EcoVadis
• Design, develop and maintain scalable ML systems
• Run large-scale experiments and tests to ensure quality and efficiency of ml- and data pipelines
• Ensure ML lifecycle monitoring by applying the best practices in MLOps and green IT
• Support large-scale data preparation and prelabelling
• Engage in building ML engineering infrastructure and systems to orchestrate batch and streaming pipelines, and leverage Infrastructure as Code (IaC) to manage and provision infrastructure
• Partner with scientists and engineers to make ML models accessible to end-users and downstream processes
• Build and enhance feature stores
• Leverage Python, MLflow,  Azure stack (e.g., Azure cloud, Azure ML, Azure Data Factory) and Databricks to deliver end-to-end solutions

You 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 discipline
• Demonstrable experience in designing ML systems and integrating those into business applications, and model monitoring at scale
• Excellent knowledge of Python, other languages are a plus
• Experience with web crawling and related frameworks like scrapy, beautifulsoup, lxml, etc. is a plus
• Deep knowledge of machine learning lifecycle, principles, and tooling
• Experience in cloud technology, preferably Azure and its ecosystem (e.g., Azure Data Factory, Azure Bicep, AzureML and Azure Cloud Storage) and Databricks
• Solid understanding of software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations
• Experience in Linux OS and scripting
• Knowledge of data management fundamentals and data storage principles
• Experience of cross-functional collaboration in the development of ML products and services (e.g., Engineers, Product Managers) is a plus
• Familiarity with (non) relational databases