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.
ResponsibilitiesFM 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.
Role Overview
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.
Qualifications
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: Enjoys working in a team environment, with the ability to effectively communicate and collaborate with technical and non-technical team members alike.
Curiosity and Innovation: Possesses a profound curiosity about AI/ML and a strong desire to explore how to improve Machine Learning Models from Design to Deployment.
Education Qualifications: A foundational knowledge of Data Science is strongly preferred. Bachelor's or higher degree in Computer Science, Statistics, Mathematics, Data Science, and/or related quantitative degree is preferred from an accredited institution.
Candidates may also qualify to be considered for Machine Learning Data Engineer III.
Compensation, Grade, and Job Title will be determined based on qualifications, experience, and technical skillset.
The position is eligible to participate in FM Global’s comprehensive Total Rewards program that includes an incentive plan, generous health and well-being programs, a 401(k) and pension plan, career development opportunities, tuition reimbursement, flexible work, paid time off allowances and much more.
FM Global is an Equal Opportunity Employer and is committed to attracting, developing, and retaining a diverse workforce.
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