MLOps, Catastrophe and Geo-Analytics

Mercury Insurance

MLOps Engineer developing machine learning applications for Mercury Insurance. Responsible for productionizing models, handling data science pipelines, and collaborating with data teams.

Posted 5/22/2026full-timeRemote • 🇺🇸 United StatesJuniorMid-Level💰 $94,458 - $179,048 per yearWebsite

Tech Stack

Tools & technologies

AWSCloudPythonSQL

About the role

Key responsibilities & impact

Develop and use modern software engineering practices to deploy ML solutions at scale, including building CI/CD pipelines and automated testing.
Work with Data Scientists and Data Engineers to build automated pipelines that train, run and monitor ML Models for business applications in an agile and elegantly orchestrated manner
Enhance and improve the code deployment and model monitoring frameworks and project operations documentation
Support life cycle management of deployed ML model life cycle management (e.g. new releases, change management, monitoring and troubleshooting)
Support the MLOps Platform, including model registry, model deployment, and feature store.
Collaborate with expert vendors and IT application teams for integrating ML models including defining SLAs and designing highly automated end-to-end testing

Requirements

What you’ll need

Minimum: Bachelor's degree in Computer Engineering, Computer Science, Mathematics, Electrical Engineering, Information Systems, or related technical field Or equivalent combination of education and/or experience
Minimum: 2 or more years of experience in MLOps engineering, data engineering, data science, and/or software engineering
Minimum: 2 or more years experience in writing SQL
Minimum: 2 or more years experience in writing Python
Preferred: Experience in P&C insurance or broader financial services industry
Preferred: Experience in modeling with Verisk, Touchstone, and or RMS
Preferred: Experience building models and processes as code
Preferred: Experience working with discrete global grid systems(like H3 or S2)
Able to multitask, prioritize, and manage time effectively
Demonstrated solid understanding, and passion for, multiple areas of MLOps engineering best practices
Experience in SQL programming
Experience in Python
Experience with cloud-based advanced data and analytics environment (e.g., AWS)
Experience with GitHub and/or GitLab
Proficient data skills and the ability to work with large structured and unstructured data sources
Excellent problem-solving skills required
Excellent analytical and critical thinking required
Excellent written and verbal communication skills required

Benefits

Comp & perks

Competitive compensation
Flexibility to work from anywhere in the United States for most positions
Paid time off (vacation time, sick time, 9 paid Company holidays, volunteer hours)
Incentive bonus programs (potential for holiday bonus, referral bonus, and performance-based bonus)
Medical, dental, vision, life, and pet insurance
401 (k) retirement savings plan with company match
Engaging work environment
Promotional opportunities
Education assistance
Professional and personal development opportunities
Company recognition program
Health and wellbeing resources, including free mental wellbeing therapy/coaching sessions, child and eldercare resources, and more

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

MLOps engineeringdata engineeringdata sciencesoftware engineeringSQLPythonautomated testingCI/CD pipelinesmodel monitoringmodel deployment

Soft Skills

multitaskingprioritizationtime managementproblem-solvinganalytical thinkingcritical thinkingwritten communicationverbal communication