Senior MLOps Engineer — Build Production ML Platform

BetMGM; North Carolina; On-site; USD 135,000 - 170,000; Full time

Benefits offered by this job

Medical Insurance

Dental Insurance

Vision Insurance

401(k) with company match

Pre-tax accounts

Flexible PTO

Professional development

Job summary

BetMGM in the United States is seeking a Senior MLOps Engineer to own the production ML platform, from feature store to live endpoints, ensuring latency, cost, and reliability budgets. You’ll work with SageMaker, Snowflake, and Terraform to ship models end‑to‑end and enable data scientists to operate with minimal ticket queues.

You will design batch and real‑time inference, implement CI/CD for ML, and establish drift and quality monitoring.

Qualifications

• BS or MS in CS, math, statistics, ML, or equivalent practical experience.
• 5+ years shipping software in production with on-call experience.
• 3+ years operating ML in production with latency/cost budgets.
• AWS SageMaker depth plus supporting services (IAM, S3, Lambda, etc.).
• Snowflake fluency with Snowpark ML and Cortex usage.
• IaC for ML using Terraform and SageMaker Pipelines.
• Champion/challenger, shadow, canary deployment experience.
• GenAI in production experience is a plus.

Responsibilities

• Stand up and operate BetMGM’s ML platform on AWS and Snowflake with Terraform.
• Build self‑service templates for end‑to‑end model deployment and monitoring.
• Design batch and real‑time inference pipelines with strict SLAs.
• Own feature store parity and model registry with CI/CD for ML.
• Implement drift detection, data quality, and model monitoring standards.

Skills

Production software

MLOps

Python

Docker

Kubernetes

CI/CD

AWS SageMaker

Snowflake

Terraform

On-call experience

Education

BS or MS in CS/Math/ML

Tools

SageMaker

Snowflake Cortex

dbt

Terraform

Evidently

Arize

Job description

BetMGM in the United States is seeking a Senior MLOps Engineer to own the production ML platform, from feature store to live endpoints, ensuring latency, cost, and reliability budgets. You’ll work with SageMaker, Snowflake, and Terraform to ship models end‑to‑end and enable data scientists to operate with minimal ticket queues.

You will design batch and real‑time inference, implement CI/CD for ML, and establish drift and quality monitoring.