ML Ops Engineer - Healthcare AI Platform

R1 RCM, Inc.; United States; On-site; USD 140,000 - 270,000; Full time

Benefits offered by this job

Top-of-market compensation + 10% bonus

Flexible PTO

Comprehensive health benefits

401(k) matching

Mission-driven teammates

Job summary

R1 RCM, Inc. is seeking a Software Engineer for the ML Ops team to own Phare OS production runtime, deploying and scaling ML models across endpoints and batch/streaming workflows.

You will implement progressive delivery, SLOs, and end-to-end observability, partnering with DevOps to ensure reproducible releases. The role emphasizes scale, reliability, and security in a production ML environment, with Terraform, Kubernetes, and CI/CD as core tools.

Qualifications

• Experience deploying and operating ML models in production (APIs and batch/streaming inference).
• Strong background in platform engineering with containerization and IaC.
• Demonstrated capability to ensure end-to-end observability,SLOs, alerting and incident response for ML systems.
• Familiarity with model registries, retraining pipelines, RBAC, secrets management and encryption.

Responsibilities

• Own the production runtime for Phare OS ML stack, including deployment, serving, and scaling across endpoints and workflows.
• Build progressive delivery pipelines with automated rollouts and rollbacks; manage latency and availability targets.
• Instrument end-to-end observability (metrics, logs, traces) and model telemetry for drift/regression; develop runbooks and incident response processes.
• Harden the platform with Terraform, Kubernetes, and CI/CD to ensure reproducible, auditable ML releases.

Skills

Production ML

Platform engineering

System reliability

Post-training lifecycles

Tools

Docker

Kubernetes

Terraform

CI/CD

Job description

R1 RCM, Inc. is seeking a Software Engineer for the ML Ops team to own Phare OS production runtime, deploying and scaling ML models across endpoints and batch/streaming workflows.

You will implement progressive delivery, SLOs, and end-to-end observability, partnering with DevOps to ensure reproducible releases. The role emphasizes scale, reliability, and security in a production ML environment, with Terraform, Kubernetes, and CI/CD as core tools.