Machine Learning Engineer

Central Insurance is hiring a Machine Learning Engineer to design, deploy, and support production machine learning solutions. The role partners with technical and business teams to move models into production, build MLOps and pricing workflows, and monitor model performance and operational health.

Responsibilities:

• 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

Requirements:

• Master's degree with a background in Computer Science, Data Science, or a related field
• 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
• 4 years related experience deploying, maintaining, and supporting machine learning models in production environments
• Individuals with temporary visas such as E, F-1(including those with OPT or CPT), H-1, H-2, L-1, B, J or TN, or who need sponsorship for work authorization now or in the future, are not eligible for hire
• 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