Senior MLOps Engineer: Production ML Platform Lead

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

Benefits offered by this job

Medical, Dental, Vision, Life, and Dis

401(k) with company match

Pre‑tax spending accounts including FS

Flexible paid time off

Professional development reimbursement

Employee resource groups

Swag, ticket giveaways, and more!

Job summary

BetMGM is seeking a Senior MLOps Engineer to own the path from trained models to production endpoints. You will build a platform for data scientists and ML engineers including a feature store, CI/CD, drift monitoring, and scalable deployment patterns.

Ideal candidates have deep AWS SageMaker and Snowflake experience, strong software engineering skills, and a track record of managing ML in production with measurable latency and cost budgets. This role supports BetMGM’s gaming tech stack in the US.

Qualifications

• 5+ years shipping software in production with Python, Docker, Kubernetes/ECS, CI/CD, and on-call experience.
• 3+ years operating ML in production with latency and cost budgets and a runbook.
• AWS SageMaker depth (Training, Endpoints, Batch Transform, Model Registry, Pipelines)
• Snowflake fluency (Snowpark ML, Cortex, dbt‑orchestrated scoring)
• IaC for ML (Terraform + SageMaker Pipelines)
• Experience with feature stores (SageMaker Feature Store, Tecton, Feast) with online/offline parity
• Champion/challenger, shadow, and canary deployment patterns
• Drift and model monitoring (Evidently, Arize, or SageMaker Model Monitor) with paging
• Software‑engineering‑first mindset for ML systems

Responsibilities

• Stand up and operate BetMGM’s ML platform on AWS (SageMaker Training, Endpoints, Batch Transform) and Snowflake with Terraform‑managed infrastructure.
• Build self‑service templates that let data scientists ship end‑to‑end models without a ticket queue.
• Design and operate batch scoring pipelines with explicit freshness and cost SLAs.
• Design and operate real‑time inference paths with sub‑100ms latency budgets and graceful degradation.
• Own the feature store with guaranteed online/offline parity.
• Implement champion/challenger, shadow deployments, and canary releases as platform primitives.

Skills

Python

Docker

Kubernetes

CI/CD

SageMaker

ML Ops

Education

BS/MS in CS/Math/Statistics/ML

Tools

Snowflake

Terraform

Evidently

Arize

Job description

BetMGM is seeking a Senior MLOps Engineer to own the path from trained models to production endpoints. You will build a platform for data scientists and ML engineers including a feature store, CI/CD, drift monitoring, and scalable deployment patterns.

Ideal candidates have deep AWS SageMaker and Snowflake experience, strong software engineering skills, and a track record of managing ML in production with measurable latency and cost budgets. This role supports BetMGM’s gaming tech stack in the US.