Lead Machine Learning Operations Engineer (PySpark, Python, R)

Overview

 

FM Global 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 Global 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.

 

Lead Machine Learning Operations Engineer (PySpark, Python, R)

 

FM Global is seeking a Lead Machine Learning Engineer to join our AI/ML team to lead 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 Lead Machine Learning Engineer you enable the Data Science team by developing platform tooling, by undertaking Data Engineering, by deploying Data Science models to production and monitoring production performance of Data Science models.  As the Lead MLOps Engineer, you will Lead 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.

 

 

Responsibilities

• Leadership: As a Lead Machine Learning Engineer on the FM Global AI/ML Ops team, you will develop data pipelines, take data science prototype models to production, fix production bugs, monitor operations and provision the necessary infrastructure in Azure.

 

• Collaboration: Work with data scientists to understand their data needs and put together data pipelines to ingest data. Work with data scientists to take data science model prototypes to production. Mentor and train junior team members.

 

• Continuous Improvement: Enhance and improve the code deployment and model monitoring frameworks and project operations documentation.

 

• Technical Competency: Design, provision and maintain the cloud infrastructure needed to support Data Engineering, Data Science, Machine Learning Engineers, and Machine Learning Operations. Write high quality code that has high test coverage, Lead code reviews to help improve code quality, help adopt best practice standards and guidelines.

 

• Passion for Technology: Work closely with IT to Stay up-to-date with the latest trends and technologies in MLOps, LLMOps, machine learning, and artificial intelligence, and share your knowledge with the team to help us stay at the forefront of the field.

 

• Technologies/Tools we use: Python, PySpark, SQL, R Studio, Databricks, Azure ADO, Azure AI/ML Data Factory, Azure (Virtual Machines, Azure Web Apps, Cloud Storage, Azure ML), Anaconda packages, Git, GitHub, GitHub Actions, Terraform, Artifactory, Airflow, Docker, Kubernetes, Linux/Windows VMs, Dynatrace, Slack for monitoring and alerting.

 

 

Qualifications

 

• Technical Knowledge: Minimum 5 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.  5 or more years of professional experience in MLOps, Data Engineering, software engineering, or a related field.

 

• Infrastructure Operations: Minimum 5 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,