Senior MLOps Engineer: Real-Time & Batch Inference

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

Benefits offered by this job

Medical Insurance

Dental Insurance

Vision Insurance

Life Insurance

Disability Insurance

401(k) with company match

Pre‑tax spending accounts

Flexible paid time off

Professional development reimbursement

Employee resource groups

Swag, ticket giveaways

Job summary

BetMGM seeks a Senior MLOps Engineer to treat ML systems as software and build the platform data scientists and ML engineers ship on, including feature stores, model registry, and CI/CD for ML on AWS.

You will own production pipelines from training to live endpoints, implement drift monitoring and cost‑aware scaling, and support real‑time and batch inference with GenAI integrations as a beneficial option.

Qualifications

• 5+ years shipping software in production.
• 3+ years operating ML in production with latency and cost budgets.
• AWS SageMaker depth (Training, Endpoints, Batch Transform, Model Registry, Pipelines).
• Snowflake fluency—Snowpark ML, Cortex, dbt‑orchestrated batch scoring.
• IaC for ML — Terraform + SageMaker Pipelines.
• Feature store ownership—online/offline parity.
• Champion/challenger, shadow, and canary deployment patterns.
• Drift and model monitoring—Evidently, Arize, WhyLabs, or SageMaker Model Monitor.
• Software‑engineering‑first mindset for ML systems.

Responsibilities

• Stand up and operate BetMGM’s ML platform on AWS and Snowflake with Terraform‑managed infra.
• Build self‑service scaffolds for end‑to‑end model shipping with CI, drift monitoring, and prebuilt connectivity.

Skills

Python

Docker

Kubernetes

CI/CD

Distributed systems

On-call

Education

BS/MS in CS/Math/Stats/ML

Tools

SageMaker

Snowflake

Terraform

ML platforms

Job description

BetMGM seeks a Senior MLOps Engineer to treat ML systems as software and build the platform data scientists and ML engineers ship on, including feature stores, model registry, and CI/CD for ML on AWS.

You will own production pipelines from training to live endpoints, implement drift monitoring and cost‑aware scaling, and support real‑time and batch inference with GenAI integrations as a beneficial option.