Machine Learning Engineer

Central Insurance Company•Van Wert, OH

2d•$102,000 - $140,000•Hybrid

About The Position

Central Insurance is hiring a Machine Learning Engineer to partner with actuaries, data scientists, software engineers, data engineers, and business stakeholders to design, deploy, and support machine learning solutions. The role focuses on moving models from experimentation into reliable, scalable, maintainable production applications, services, and decision-support workflows aligned with business objectives. The position develops and supports deployment and CI/CD processes, APIs and scoring workflows, pricing data pipelines, and model monitoring and observability practices. It also contributes to MLOps tooling and reusable engineering standards, supports model validation and production troubleshooting, and applies appropriate data governance, security, privacy, and documentation practices. The role is full-time and may be hybrid or remote depending on location. Listed locations are Van Wert, Ohio; Dublin, Ohio; Waltham, Massachusetts; Alpharetta, Georgia; and Irving, Texas. The hiring range is $102,000–$140,000, representing typical starting salaries and a portion of the full pay range. Central Insurance does not provide work authorization sponsorship for this position.

Requirements

Master’s degree in Computer Science, Data Science, or a related field; or a bachelor’s degree in one of these fields and 2 years of related experience deploying, maintaining, and supporting production machine learning models; or 4 years of related experience deploying, maintaining, and supporting production machine learning models.
Legally authorized to work in the United States; sponsorship is not provided, and applicants who need sponsorship now or in the future are not eligible.

Nice To Haves

Experience developing CI/CD pipelines, automated testing, model packaging, release management, and rollback processes for machine learning or software applications.
Experience with containerization, orchestration, or infrastructure automation tools such as Docker, Kubernetes, Terraform, or similar technologies.
Experience with model monitoring, observability, logging, alerting, performance tracking, data drift detection, or production health monitoring.
Programming experience in Python or similar languages used for machine learning engineering, automation, API development, or data pipeline development.
Experience with SQL, data validation, data quality checks, and data pipeline support for model scoring, retraining, monitoring, or reporting workflows.
Experience developing or supporting APIs, microservices, batch scoring processes, or real-time inference services.
Experience with version control, Git-based workflows, code review practices, documentation, and reproducible development standards.

Responsibilities

Design, build, test, deploy, and support reliable, scalable, maintainable machine learning solutions aligned with business objectives.
Partner with data scientists, actuaries, data engineers, software engineers, and business stakeholders to transition models into production-ready applications, services, and decision-support workflows.
Develop and maintain automated deployment and CI/CD processes for machine learning models and pricing solutions, including packaging, versioning, validation, release management, rollback procedures, automated testing, and environment consistency.
Develop and support APIs, services, application integration patterns, and batch or real-time scoring workflows for model inference and business-system consumption.
Support pricing data pipeline development and validation for model scoring, monitoring, retraining, and reporting, partnering with data engineering teams on larger data integration needs.
Implement model monitoring, observability, and logging to track model performance, data drift, prediction quality, system reliability, latency, dependencies, integrations, and production health.
Contribute to model registry, feature store, experiment tracking, reusable engineering patterns, templates, tests, documentation, and MLOps tooling.
Apply data governance, security, privacy, and documentation practices to model deployment and monitoring activities, emphasizing traceability, reproducibility, and responsible data use.
Participate in model validation, QA testing, output review, performance tuning, and production workflow optimization to meet stakeholder, compliance, operational, reliability, maintainability, and cost-effectiveness requirements.
Troubleshoot production issues involving machine learning models, data dependencies, model outputs, and integration points; recommend improvements to reduce operational risk.
Maintain awareness of industry trends, emerging technologies, and modern MLOps practices, and recommend practical improvements to the company’s machine learning engineering capabilities.
Provide technical guidance, peer review, and knowledge sharing on production machine learning practices, deployment standards, monitoring, and operational support.