Senior MLOps Engineer: Scale AI in Production

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

Benefits offered by this job

Medical insurance

401(k) with company match

Flexible PTO

Professional development reimbursement

Employee resource groups

Swag and ticket giveaways

Job summary

BetMGM in the United States is seeking a Senior MLOps Engineer to own the path from trained models to production endpoints with latency and cost budgets, across batch and real-time inference on SageMaker and Snowflake Cortex. You’ll build a platform with a feature store, model registry, and CI/CD for ML.

You will collaborate with data scientists and analytics partners, implement drift monitoring, and scale endpoints while prioritizing reliability, observability, and on‑call readiness.

Qualifications

• BS or MS in Computer Science, Math, Statistics, Machine Learning, or related STEM field; practical experience counts
• 5+ years shipping software in production (Python, Docker, Kubernetes, CI/CD)
• 3+ years operating ML in production with latency and cost budgets
• Deep AWS SageMaker experience and Snowflake/dbt batch scoring
• IaC experience with Terraform or similar tools

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 deployment
• Design and operate batch and real‑time inference pipelines with explicit SLAs
• Own the feature store with online/offline parity
• Implement CI/CD, model provenance, and drift/quality monitoring
• Drive responsible AI practices and incident response for production models

Skills

Distributed systems debugging

On-call readiness

Collaboration with data scientists

Education

BS or MS in Computer Science or related field

Tools

Python

Docker

Kubernetes

ECS

CI/CD

SageMaker

Snowflake

Terraform

Feature store (SageMaker/Feast/Tecton)

Job description

BetMGM in the United States is seeking a Senior MLOps Engineer to own the path from trained models to production endpoints with latency and cost budgets, across batch and real-time inference on SageMaker and Snowflake Cortex. You’ll build a platform with a feature store, model registry, and CI/CD for ML.

You will collaborate with data scientists and analytics partners, implement drift monitoring, and scale endpoints while prioritizing reliability, observability, and on‑call readiness.