ML Platform Engineer for Production & Observability

Credit Acceptance Corporation; United States; Remote; USD 154,000 - 226,000; Full time

Job summary

Credit Acceptance seeks an experienced ML Platform Engineer to own the production ML/AI platform: pipelines, registry, serving, evaluation, and cost management. This is an operations and infrastructure role, not a modeling role, with success measured by uptime, safe deployments, and observable performance.

The role requires 7+ years building production ML systems, strong Python/SQL, cloud experience (AWS), and hands-on with Databricks, MLflow, and OpenTelemetry.

Qualifications

• Bachelor’s or Master’s degree in a technical field with relevant ML/AI experience.
• 5+ years building and operating production ML/AI systems.
• Proven ownership of ML/AI services through full life cycle.
• Strong Python and SQL with production-grade practices and CI/CD.
• Cloud ML platform experience; AWS and Databricks preferred.

Responsibilities

• Own end-to-end deployment paths for ML/GenAI models including pipelines and registry.
• Manage runtime health, monitoring, alerts, drift detection, and costing.
• Operate gateway/proxy surfaces and enforce least-privilege access.
• Evaluate agents in production with online scoring and drift detection.
• Maintain observability substrates, logs, and data contracts.

Skills

Python

SQL

CI/CD

Production ML

Model serving

Monitoring

Cost optimization

Education

Bachelor’s degree in Computer Science, Engineering, Statistics or relevant field

Master’s degree in one of those fields

Tools

AWS

Databricks

MLflow

OpenTelemetry

Dynatrace

Docker

Kubernetes

Job description

Credit Acceptance seeks an experienced ML Platform Engineer to own the production ML/AI platform: pipelines, registry, serving, evaluation, and cost management. This is an operations and infrastructure role, not a modeling role, with success measured by uptime, safe deployments, and observable performance.

The role requires 7+ years building production ML systems, strong Python/SQL, cloud experience (AWS), and hands-on with Databricks, MLflow, and OpenTelemetry.