Senior MLOps Platform Engineer

OfferGenie; New York (NY); Hybrid; USD 298,000 - 351,000; Full time

Job summary

Garner Health is seeking a Staff Machine Learning Operations Engineer to join our Platform Engineering team in New York City. You will lead the reliability, performance, and cost-efficiency of production ML systems, enable secure deployment of models, and drive scalable ML infrastructure in a fast-growing healthcare company.

The role reports to the VP of Platform Engineering and requires 7+ years of software engineering in ML production.

Qualifications

• 7+ years of software engineering experience in production ML/data systems.
• Deep experience with the modern ML production stack: model serving, feature stores, registries, and CI/CD for ML.
• Strong infrastructure and platform engineering fundamentals: Kubernetes, cloud, Terraform/IaC, observability, and incident response.
• Experience designing ML platforms or significant components of one and ability to decide when to build vs. buy.

Responsibilities

• Own the reliability, performance, functionality, and cost-efficiency of Garner's production ML systems, including SLOs and on-call responsibilities.
• Architect Garner's ML platform with data infrastructure, feature store, model registry, and CI/CD for models.
• Implement ML-specific CI/CD pipelines with automated data quality checks and model validation before deployment.
• Reduce cost and latency via improved architecture, hardware choices, and model optimization.
• Lay the foundation for a future MLOps team with workflows, standards, and KPIs for fast teammate onboarding.
• Establish drift monitoring and groundwork for Continuous Training architectures.

Skills

MLOps

Kubernetes

CI/CD for ML

Terraform/IaC

Observability

Cloud AWS

Model serving (Sagemaker/Triton)

Tools

Sagemaker

Triton

Feature store

Model registry

CI/CD pipelines

Job description

Garner Health is seeking a Staff Machine Learning Operations Engineer to join our Platform Engineering team in New York City. You will lead the reliability, performance, and cost-efficiency of production ML systems, enable secure deployment of models, and drive scalable ML infrastructure in a fast-growing healthcare company.

The role reports to the VP of Platform Engineering and requires 7+ years of software engineering in ML production.