Machine Learning Operations Data Engineer IV - Factory Mutual Insurance Company

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 IV to join our AI/ML team to Head 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.

Minimum 3 years of hands-on experience implementing AI/ML solutions and platform tooling for Data Science considered, 6+ years highly preferred.

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.

• Essential Qualifications
  • Bachelor's degree in Computer Science, Data Science, or related field
  • 8+ years of experience in MLOps, data engineering, or software development
  • Strong proficiency in both R and Python programming languages
  • Preferred Extensive experience with Databricks platform and MLflow

• Technical Skills
  • Programming: Expert-level knowledge in translating programming in Python, maintaining functionality and performance
  • Solution Architecture: Ability to design and implement scalable MLOps solutions
  • DevOps: Experience in establishing and maintaining CI/CD pipelines for machine learning workflows
  • Prefer to have Databricks experience: In-depth knowledge of Databricks platform, including Delta Lake and Spark
  • MLflow: Proficiency in using MLflow for model tracking, versioning, and deployment
  • Monitoring and Alerting: Experience in setting up monitoring systems and alerts for ML models in production

• Key Responsibilities
  • Lead the refactoring of existing R codebase to Python, ensuring code quality and performance optimization
  • Design and implement MLOps architecture solutions that align with best practices and organizational needs
  • Establish and maintain robust DevOps pipelines for continuous integration and deployment of ML models
  • Configure and manage MLflow on Databricks for model lifecycle management
  • Implement monitoring systems to track model performance, data drift, and system health
  • Set up alerting mechanisms to promptly notify stakeholders of any issues in the ML pipeline
  • Collaborate with data scientists, engineers, and business stakeholders to ensure smooth integration of ML models into production environments

• Preferred Qualifications
  • Experience with cloud platforms (AWS, Azure, or GCP, Azure is a MUST have)
  • Knowledge of containerization technologies (Docker, Kubernetes)
  • Familiarity with data versioning tools (DVC, Pachyderm)
  • Experience with automated testing frameworks for ML models
  • Understanding of data privacy and security best practices

• Soft Skills
  • Strong problem-solving and analytical skills
  • Excellent communication abilities to explain technical concepts to non-technical stakeholders
  • Ability to work effectively in a collaborative, fast-paced environment
  • Proactive approach to identifying and resolving potential issues in ML pipelines

Compensation, Grade, and Job Title will be determined based on qualifications, experience, and technical skillset.

The position is eligible to participate in FM'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 is an Equal Opportunity Employer and is committed to attracting, developing, and retaining a diverse workforce.

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