Senior MLOps Engineer — AI Platform & Deployment

NRG; Beaverton (OR); Hybrid; USD 150,000 - 185,000; Full time

Benefits offered by this job

Paid holidays

Employee discounts

Medical/dental/vision/life coverage &

401(k) + Employer Match

Employee Resource Groups

Job summary

NRG is seeking a Sr MLOps Engineer to design and scale the model lifecycle, deployment, observability, and foundational infrastructure for multiple AI features. You will build registry and serving systems, own versioning across models, datasets, and prompts, and standardize workflows for AI teams.

You will collaborate with engineering, data science, product, and operations to productionize AI capabilities at scale, improving reliability, latency, and cost efficiency across deployments.

Qualifications

• Bachelor’s or Master’s degree in a related field with 5+ years in software development, applied science, or ML engineering.

Responsibilities

• Build model registry, model serving, deployment, rollback, and CI/CD systems for production AI services.
• Own feature, dataset, model, and prompt versioning patterns across AI products.
• Standardize training, evaluation, release, monitoring, and operational workflows for AI teams.
• Improve reliability, cost efficiency, latency, and repeatability of AI launches.
• Create reusable platform patterns across AI features.
• Partner with engineering, data science, product, and operations teams to productionize AI capabilities at scale.

Skills

Python

Cloud engineering

CI/CD

Git

IaC

ML engineering

Model registry

MLOps

Education

Bachelor's degree in CS/SE/AI/ML

Master's degree in CS/SE/AI/ML

Tools

GCP

AWS

Kubernetes

Vertex AI

SageMaker

MLflow

Terraform

Job description

NRG is seeking a Sr MLOps Engineer to design and scale the model lifecycle, deployment, observability, and foundational infrastructure for multiple AI features. You will build registry and serving systems, own versioning across models, datasets, and prompts, and standardize workflows for AI teams.

You will collaborate with engineering, data science, product, and operations to productionize AI capabilities at scale, improving reliability, latency, and cost efficiency across deployments.