Senior ML Production Platform Engineer

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

Benefits offered by this job

Health benefits

401(k) matching

FSA & commuter

Flexible PTO

Training programs

Employee groups

Swag & tickets

Job summary

BetMGM is seeking a Senior MLOps Engineer to own the production ML platform across batch and real‑time paths. You will build end‑to‑end pipelines, manage a feature store, and ensure latency and cost budgets are met.

The role emphasizes a software‑engineering mindset with observability and on‑call readiness. You will leverage SageMaker, Snowflake, Terraform, and CI/CD to deliver scalable ML systems, with GenAI integrations as a plus.

Qualifications

• 5+ years shipping software in production with Python and distributed systems.
• 3+ years operating ML in production with latency and cost budgets.
• AWS SageMaker depth across Training/Endpoints/Batch Transform and related services.
• Snowflake (Snowpark ML, Cortex, batch scoring) experience.
• IaC for ML (Terraform + SageMaker Pipelines) and automated deployments.
• Ownership of a feature store with online/offline parity.

Responsibilities

• Stand up and run BetMGM’s ML platform on AWS and Snowflake with Terraform‑driven infra.
• Build self‑service templates for end‑to‑end model delivery with CI, drift monitoring, and pre‑baked connectivity.
• Design and operate batch scoring pipelines and real‑time inference paths with latency targets.
• Own feature store parity and manage model registry, CI/CD, and deployment lineage.
• Implement champion/challenger, shadow deployments, and canary releases as standard patterns.
• Maintain drift detection, data quality, and model monitoring with paging and runbooks.

Skills

Python

Docker

Kubernetes

CI/CD

Distributed systems

On-call

SageMaker

Snowflake

Terraform

Feature Store

Education

BS/MS in CS/Math/ML or related field

Tools

SageMaker

Snowflake

Terraform

Evidently/Arize

Job description

BetMGM is seeking a Senior MLOps Engineer to own the production ML platform across batch and real‑time paths. You will build end‑to‑end pipelines, manage a feature store, and ensure latency and cost budgets are met.

The role emphasizes a software‑engineering mindset with observability and on‑call readiness. You will leverage SageMaker, Snowflake, Terraform, and CI/CD to deliver scalable ML systems, with GenAI integrations as a plus.