ML Ops Engineer — Production AI Platform

R37 Lab, R1 RCM; New York (NY); Hybrid; USD 140,000 - 300,000; Full time

Benefits offered by this job

Top-of-market compensation

Hybrid in-office (min. 3 days per week

Comprehensive health benefits

401(k) matching

Mission-driven teammates

Job summary

R1 RCM Inc. in New York City is hiring for senior ML Ops engineers to own the production runtime of Phare’s ML stack, deploying and scaling models across inference endpoints and batch workflows.

You will build progressive delivery pipelines with automated rollouts, manage latency and availability SLAs, and instrument complete observability for drift and regression. The role requires 5+ years in software and ML Ops, with hands-on experience in GPUs in production, platform engineering with

Qualifications

• 5+ years software engineering experience with 2+ years ML Ops.
• Experience deploying and operating models in production on GPUs (APIs and batch/streaming).
• Strong with Docker/Kubernetes, IaaC (Terraform) and CI/CD for services and model artifacts.
• Ability to maintain environment parity, reproducible releases, and robust model/experiment versioning with data lineage.

Responsibilities

• Own the production runtime for Phare/R1 RCM ML stack: deploying, serving, and scaling models across endpoints and batch/streaming workflows.
• Build progressive delivery pipelines with automated rollouts and rollbacks; manage SLOs for latency and availability.
• Instrument end-to-end observability (metrics, logs, traces, drift, regression) and actionable alerting.
• Hardening the platform with Terraform, Kubernetes, and CI/CD to ensure reproducible, auditable ML releases.

Skills

Production ML

Platform engineering

System Reliability

RBAC & security

Tools

Docker/Kubernetes

Terraform

CI/CD

Data lineage

Job description

R1 RCM Inc. in New York City is hiring for senior ML Ops engineers to own the production runtime of Phare’s ML stack, deploying and scaling models across inference endpoints and batch workflows.

You will build progressive delivery pipelines with automated rollouts, manage latency and availability SLAs, and instrument complete observability for drift and regression. The role requires 5+ years in software and ML Ops, with hands-on experience in GPUs in production, platform engineering with