Machine Learning Operations Engineer II

FM is a leading property insurer of the world's largest businesses, providing more than one-third of FORTUNE 1000-size companies with engineering-based risk management and property insurance solutions. FM helps clients maintain continuity in their business operations by drawing upon state-of-the-art loss-prevention engineering and research; risk management skills and support services; tailored risk transfer capabilities; and superior financial strength. To do so, we rely on a dynamic, culturally diverse group of employees, working in more than 100 countries, in a variety of challenging roles. FM Global is seeking a Machine Learning Operations Data Engineer II to join our AI/ML team to support Machine Learning Engineering, working very closely with Data Science, Data Engineering, Subject Matter Experts and Solution Architecture teams. As a part of our dynamic team, you will be an Azure AI/ML Ops Engineer focused on building a robust data platform and pipelines that enable advanced analytics. This role offers the unique opportunity to develop AI/ML-based applications that have a meaningful impact on our customers. Our machine learning platform helps manage the various components of the ML application development life cycle, starting from data ingestion, and experimentation, to model training, deployment, and monitoring. All of these components are interdisciplinary, so you will be working closely with cross-functional teams across the organization.

As a Machine Learning Operations Data Engineer II you will develop platform tooling, deploy data science models to production and monitor production performance. You will support Machine Learning projects end-to-end and develop platform tooling for the Data Science team. You will be responsible for Machine Learning Operations outcomes: Velocity of Model Deployments, Validation of Model Deployed Code and Versioning of Data, Model and Infrastructure. Minimum 3 years of hands-on experience implementing AI/ML solutions and platform tooling for Data Science. Expert in Spark SQL, PySpark, (Python and/or R programming language) which includes experience in libraries such as Pandas, scikit-learn, R (tidyverse, glm, caret etc…), MLFlow, Experimentation, Tracking, Productionizing and proficient in SQL. Three or more years of professional experience in MLOps, Data Engineering, software engineering, or a related field. - Infrastructure Operations: Minimum 3 to 7+ years of hands-on experience in some combination of the following technologies: Azure (VMs, Web Apps, Managed Databases), GitHub Actions, Terraform, Packer, Airflow, Docker, Kubernetes, Linux/Windows VM administration, Shell scripting (primary Bash but PowerShell as well). A solid understanding of modern security and networking principles and standards. Knowledge of best practices in software engineering is necessary. - Collaborative Spirit