Senior Machine Learning Operations Engineer

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Ready to make your career legendary? Join us as we bring the magic of Vegas to our players. The BetMGM team has over 1,400 talented members, revolutionizing sports betting and online gaming in the United States and Canada. We’re a brand with technology at our hearts and the most driven and focused talent in the business.

• 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!

The Senior MLOps Engineer treats ML systems as software systems and owns the path from a trained model to a production endpoint that meets its latency, cost, and reliability budgets — across both batch scoring (Sage Maker Batch Transform, Snowflake Cortex / Snowpark ML, dbt‑orchestrated scoring) and real‑time inference (Sage Maker real‑time endpoints, Lambda + Bedrock, sub‑second feature serving). The Senior Engineer builds the platform that data scientists and ML engineers ship on: feature store with guaranteed online/offline parity, model registry, CI/CD for ML, drift and quality monitoring, champion/challenger and shadow deployment scaffolding.

This requires a software‑engineering‑first mindset — distributed systems, observability, and on‑call instincts are the foundation; ML literacy makes them effective for this role. GenAI integration experience is a plus, not a requirement.

• Stand up and operate BetMGM’s ML platform on AWS (Sage Maker 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 — Sage Maker Batch Transform, dbt‑orchestrated scoring against Snowflake, Snowpark ML — with explicit freshness and cost SLAs.
• Design and operate real‑time inference paths — Sage Maker 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 (Sage Maker Feature Store, Tecton, or Feast) with guaranteed online/offline parity — training‑serving skew is treated as an incident, not a trade‑off.

• 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 Sage Maker 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 post‑mortems.

• 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.
• Partner with the Helix team on AI personalization workloads as they ramp toward March Madness 2027.

• Direct AI coding agents (Claude Code, Cursor, Git Hub Copilot, dbt Copilot) as a force multiplier across infrastructure code, eval suites, and model‑serving glue — designing work for agents to do, not just accepting their suggestions.

• Partner with the data engineering team on shared standards (Terraform modules, CI/CD patterns, observability, lineage).
• Work alongside data scientists and analytics partners to…