Machine Learning Engineer
Alpharetta, GA, United States17h ago
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Key details
- Salary
- $102K–$140K/yr
- Location
- Alpharetta, GA, United States
- Work style
- Hybrid
- Level
- Mid level
- Job type
- Full-time
- Experience
- 2–5 yrs
- Function
- Data & ML
- Focus area
- AI / ML
- Posted
- 17h ago
Skills
ci/cdcode reviewscontainerizationcustomer servicedata governancedata integrationdata pipelinesdata qualitydata sciencedata validationdockerexperiment trackingfeature storegitinfrastructure automationkubernetesmachine learningmicroservicesmlopsmodel deploymentobservabilityoperational risk managementpythonqa testingrelease managementsqlterraformtest automationversion control
About the role
Location: Van Wert, OH; Dublin, OH; Waltham, MA; Alpharetta, GA; Irving, TX
Work Model: Hybrid or Remote based on location
Position type: Full-Time
About the Company
Central Insurance has been family owned and operating since 1876, and we’re just getting started. We’re a property and casualty insurance group that believes great work starts with Integrity, thrives on strong Relationships, and never settles for less than Excellence. With over $2B in assets and an A (Excellent) A.M. Best rating, we offer stability, opportunity, and a team you’ll actually enjoy being part of. We make a difference for our policyholders by providing excellent service and being there in the greatest times of need.
About the Role
We are a team of employees who are passionate to deliver best in-class customer service and innovation in the industry. It's because we put Integrity, Relationships and Excellence in all aspects of our work.
Our employees have the opportunity to fully utilize their talents and bring their best self. We believe that who you are is just as important as what you do!
If you are a problem-solver with a service first mentality and a passion for helping people, come join our team!
As a Machine Learning Engineer, you will be instrumental by partnering with actuaries, data scientists, software engineers, and business stakeholders to design and deploy machine learning solutions.
What we are looking for
Master’s degree with a background in Computer Science, Data Science, or a related field
Or Bachelor’s degree with a background in Computer Science, Data Science, or a related field and 2 years of related experience deploying, maintaining, and supporting machine learning models in production environments
Or 4 years related experience deploying, maintaining, and supporting machine learning models in production environments
Experience developing CI/CD pipelines, automated testing, model packaging, release management, and rollback processes for machine learning or software applications- preferred
Experience with containerization, orchestration, or infrastructure automation tools such as Docker, Kubernetes, Terraform, or similar technologies- preferred
Experience with model monitoring, observability, logging, alerting, performance tracking, data drift detection, or production health monitoring- preferred
Programming experience in Python or similar languages used for machine learning engineering, automation, API development, or data pipeline development- preferred
Experience with SQL, data validation, data quality checks, and data pipeline support for model scoring, retraining, monitoring, or reporting workflows- preferred
Experience developing or supporting APIs, microservices, batch scoring processes, or real-time inference services- preferred
Experience with version control, Git-based workflows, code review practices, documentation, and reproducible development standards- preferred
What you’ll do
Designs, builds, tests, deploys, and supports machine learning solutions that are reliable, scalable, maintainable, and aligned to business objectives.
Partners with data scientists, actuaries, data engineers, software engineers, and business stakeholders to transition models from experimentation into production-ready applications, services, and decision-support workflows.
Develops and maintains automated deployment and CI/CD processes for machine learning models and pricing solutions, including model packaging, versioning, validation, release management, rollback procedures, automated testing, environment consistency, and repeatable release practices.
Develops and supports APIs, services, application integration patterns, and batch or real-time scoring workflows that enable reliable model inference and consumption of model outputs by business systems.
Supports pricing data pipeline development and validation needed for model scoring, monitoring, retraining, and reporting; partners with data engineering teams on larger data integration needs.
Implements model monitoring, observability, and logging practices to track model performance, data drift, prediction quality, system reliability, latency, data dependencies, integration points, and other indicators of production health.
Contributes to model registry, feature store, experiment tracking, reusable engineering patterns, templates, tests, documentation, and related MLOps tooling to improve traceability, reproducibility, deployment consistency, lifecycle management, and ongoing support.
Applies appropriate data governance, security, privacy, and documentation practices to model deployment and monitoring activities, with emphasis on traceability, reproducibility, and responsible use of data.
Participates in model validation, QA testing, output review, performance tuning, and production workflow optimization to ensure deployed solutions perform as expected and meet stakeholder, compliance, operational, reliability, maintainability, and cost-effectiveness requirements.
Troubleshoots production issues related to machine learning models, data dependencies, model outputs, and integration points in collaboration with software engineers; recommends improvements to reduce operational risk.
Maintains awareness of industry trends, emerging technologies, and modern MLOps practices; recommends practical improvements to Central Insurance’s machine learning engineering capabilities.
Provides technical guidance, peer review, and knowledge sharing to team members on production machine learning practices, deployment standards, monitoring approaches, and operational support.
Hiring Range
$102,000–$140,000 represents typical starting salaries and is a portion of the full pay range for the role.
Starting pay is determined based on several factors, including relevant experience, education, skills, and certifications. The full pay range is broader and is designed to provide employees with meaningful opportunities for salary growth as they develop their skills, increase proficiency, and advance in the role.
Total Rewards
Central establishes base pay based on several factors including labor market data and an evaluation of candidate qualifications relative to role requirements. Base pay is one component of a comprehensive total rewards package designed to support employees’ financial, health, career, and retirement objectives.
Central provides extensive health and wellness benefits to promote flexibility, work-life balance, generous paid leave, and long-term financial security.
For more information, see Central Insurance Benefits.
Work Model: Hybrid or Remote based on location
Position type: Full-Time
About the Company
Central Insurance has been family owned and operating since 1876, and we’re just getting started. We’re a property and casualty insurance group that believes great work starts with Integrity, thrives on strong Relationships, and never settles for less than Excellence. With over $2B in assets and an A (Excellent) A.M. Best rating, we offer stability, opportunity, and a team you’ll actually enjoy being part of. We make a difference for our policyholders by providing excellent service and being there in the greatest times of need.
About the Role
We are a team of employees who are passionate to deliver best in-class customer service and innovation in the industry. It's because we put Integrity, Relationships and Excellence in all aspects of our work.
Our employees have the opportunity to fully utilize their talents and bring their best self. We believe that who you are is just as important as what you do!
If you are a problem-solver with a service first mentality and a passion for helping people, come join our team!
As a Machine Learning Engineer, you will be instrumental by partnering with actuaries, data scientists, software engineers, and business stakeholders to design and deploy machine learning solutions.
What we are looking for
Master’s degree with a background in Computer Science, Data Science, or a related field
Or Bachelor’s degree with a background in Computer Science, Data Science, or a related field and 2 years of related experience deploying, maintaining, and supporting machine learning models in production environments
Or 4 years related experience deploying, maintaining, and supporting machine learning models in production environments
Experience developing CI/CD pipelines, automated testing, model packaging, release management, and rollback processes for machine learning or software applications- preferred
Experience with containerization, orchestration, or infrastructure automation tools such as Docker, Kubernetes, Terraform, or similar technologies- preferred
Experience with model monitoring, observability, logging, alerting, performance tracking, data drift detection, or production health monitoring- preferred
Programming experience in Python or similar languages used for machine learning engineering, automation, API development, or data pipeline development- preferred
Experience with SQL, data validation, data quality checks, and data pipeline support for model scoring, retraining, monitoring, or reporting workflows- preferred
Experience developing or supporting APIs, microservices, batch scoring processes, or real-time inference services- preferred
Experience with version control, Git-based workflows, code review practices, documentation, and reproducible development standards- preferred
What you’ll do
Designs, builds, tests, deploys, and supports machine learning solutions that are reliable, scalable, maintainable, and aligned to business objectives.
Partners with data scientists, actuaries, data engineers, software engineers, and business stakeholders to transition models from experimentation into production-ready applications, services, and decision-support workflows.
Develops and maintains automated deployment and CI/CD processes for machine learning models and pricing solutions, including model packaging, versioning, validation, release management, rollback procedures, automated testing, environment consistency, and repeatable release practices.
Develops and supports APIs, services, application integration patterns, and batch or real-time scoring workflows that enable reliable model inference and consumption of model outputs by business systems.
Supports pricing data pipeline development and validation needed for model scoring, monitoring, retraining, and reporting; partners with data engineering teams on larger data integration needs.
Implements model monitoring, observability, and logging practices to track model performance, data drift, prediction quality, system reliability, latency, data dependencies, integration points, and other indicators of production health.
Contributes to model registry, feature store, experiment tracking, reusable engineering patterns, templates, tests, documentation, and related MLOps tooling to improve traceability, reproducibility, deployment consistency, lifecycle management, and ongoing support.
Applies appropriate data governance, security, privacy, and documentation practices to model deployment and monitoring activities, with emphasis on traceability, reproducibility, and responsible use of data.
Participates in model validation, QA testing, output review, performance tuning, and production workflow optimization to ensure deployed solutions perform as expected and meet stakeholder, compliance, operational, reliability, maintainability, and cost-effectiveness requirements.
Troubleshoots production issues related to machine learning models, data dependencies, model outputs, and integration points in collaboration with software engineers; recommends improvements to reduce operational risk.
Maintains awareness of industry trends, emerging technologies, and modern MLOps practices; recommends practical improvements to Central Insurance’s machine learning engineering capabilities.
Provides technical guidance, peer review, and knowledge sharing to team members on production machine learning practices, deployment standards, monitoring approaches, and operational support.
Hiring Range
$102,000–$140,000 represents typical starting salaries and is a portion of the full pay range for the role.
Starting pay is determined based on several factors, including relevant experience, education, skills, and certifications. The full pay range is broader and is designed to provide employees with meaningful opportunities for salary growth as they develop their skills, increase proficiency, and advance in the role.
Total Rewards
Central establishes base pay based on several factors including labor market data and an evaluation of candidate qualifications relative to role requirements. Base pay is one component of a comprehensive total rewards package designed to support employees’ financial, health, career, and retirement objectives.
Central provides extensive health and wellness benefits to promote flexibility, work-life balance, generous paid leave, and long-term financial security.
For more information, see Central Insurance Benefits.