Job Title

The ML / MLOps Engineering team is responsible for the successful implementation and iteration of AI/ML solutions including providing architectural guidance and development support. Some of our focus areas include: developing reusable frameworks, code optimization and refactoring, scaling up ML solutions, and foreseeing and testing for common issues that may arise in production. We are looking for a highly capable Senior ML or MLOps Engineer with a strong Software Engineering and DevOps background. As a Senior MLOps Engineer, you will be embedded and supporting a revenue generation or cost optimization project, ensuring its success in production by improving the code, creating automated CI/CD testing, and developing frameworks that can be reused for other similar projects. Activities: Build, maintain, and document machine learning frameworks (python packages) used across multiple projects. · Support a project team with Data Scientists, Business Stakeholders, Analysts, and Data Engineers. · Develop reusable feature stores for rules-based and AI/ML models. · Implement monitoring capabilities for model performance and effectiveness in production. · Automate CI/CD testing and deployments incorporating MLOps best practices. Skills: -Bachelor's degree in software engineering, computer science, data science, mathematics, or a related field. · 5 years of overall experience in Data Analytics. · 3 years of experience with ML Engineering and/or ML Ops. Up to 2 years of Software Engineering or Data Engineering experience can also count towards this requirement. · Sharp critical thinking skills and ability to learn and question complex processes and solutions. · Experience building scalable machine learning systems and data-driven products working with cross-functional teams. · Experience creating python packages · Well-developed software engineering skills, including use of proper development, QA, and production environments, object-oriented programming, version control, and knowledge of multiple programming languages. · Proficiency in Python and experience with common data analytics packages (e.g. Numpy, Pandas, Sklearn, PySpark). · Proficiency in SQL and Azure · Good communication skills and the ability to understand and synthesize requirements across multiple project domains.