Senior Machine Learning Operations Engineer

BetMGM

Senior MLOps Engineer building and operating BetMGM's machine learning platform on AWS and Snowflake. Collaborating with data scientists to ensure smooth production deployment and monitoring.

Posted 7/10/2026full-timeRemote • Minnesota, Montana, Nevada, New Jersey, New York • 🇺🇸 United StatesSenior💰 $135,000 - $170,000 per yearWebsite

Tech Stack

Tools & technologies

AWSDistributed SystemsDockerKubernetesPythonTerraform

About the role

Key responsibilities & impact

Stand up and operate BetMGM's ML platform on AWS (SageMaker Training, Model Registry, Pipelines, Endpoints, Batch Transform) and Snowflake (Snowpark ML, Cortex), with Terraform-managed infrastructure.
Build self-service scaffolds that let data scientists ship a model end-to-end without a ticket queue — cookie-cutter project templates with CI, drift monitoring, alerting, IaC, and Snowflake connectivity pre-baked.
Design and operate batch scoring pipelines — SageMaker Batch Transform, dbt-orchestrated scoring against Snowflake, Snowpark ML — with explicit freshness and cost SLAs.
Design and operate real-time inference paths — SageMaker real-time endpoints, Lambda + Bedrock for GenAI, API Gateway — with stated latency budgets (typically sub-100ms) and graceful degradation under load.
Own the feature store (SageMaker Feature Store, Tecton, or Feast) with guaranteed online/offline parity — training-serving skew is treated as an incident, not a tradeoff.
Build CI/CD for ML — model registry, automated retraining triggers, model versioning, lineage from feature → training run → deployed model → live prediction.
Implement champion/challenger, shadow deployments, and canary releases as platform primitives so individual model teams do not reinvent them per project.
Stand up drift detection, data quality, and model performance monitoring (Evidently, Arize, or SageMaker Model Monitor — pick one and standardize) with paging that routes to humans who can fix it.
Own MLOps incident response — production model failures are SEV events with postmortems.
Right-size endpoints, batch caching, request batching, and autoscaling. State cost-per-prediction targets up front and meet them.
Integrate LLM APIs (Bedrock, Anthropic, OpenAI) into production paths — RAG pipelines, agent eval frameworks, prompt versioning, cost and latency observability.

Requirements

What you’ll need

BS or MS in Computer Science, Math, Statistics, Machine Learning, or other STEM field — or equivalent practical experience.
5+ years shipping software in production — Python, Docker, Kubernetes or ECS, CI/CD, distributed systems debugging — including time on-call.
3+ years operating ML in production — you have owned a model in prod that served real traffic, with stated latency and cost budgets and a runbook you wrote.
AWS depth across the SageMaker surface (Training, Endpoints, Batch Transform, Model Registry, Pipelines).
Snowflake fluency — Snowpark ML, Cortex, dbt-orchestrated batch scoring, RBAC for ML workloads.
IaC for ML — Terraform + SageMaker Pipelines or equivalent. No manual console deployments to production.
Feature store experience — SageMaker Feature Store, Tecton, or Feast — with explicit ownership of online/offline parity.
Champion/challenger, shadow, and canary deployment patterns as production muscle, not blog-post familiarity.
Drift and model monitoring — Evidently, Arize, WhyLabs, or SageMaker Model Monitor — wired to a paging path.
Software-engineering-first mindset — you treat ML systems as systems, not notebooks.

Benefits

Comp & perks

Medical, Dental, Vision, Life, and Disability Insurance
401(k) with company match
Pre-tax spending accounts including health care FSA and commuter savings
Flexible paid time off
Professional development reimbursement and ongoing skills training opportunities
Employee resource groups
Swag, ticket giveaways, and more!

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

Machine LearningModel MonitoringCI/CDDistributed Systems DebuggingBatch ScoringReal-Time InferenceFeature Store ManagementDrift DetectionModel Performance MonitoringInfrastructure as Code (IaC)

Soft Skills

Problem-SolvingCollaborationIncident Response