Senior Machine Learning Operations Engineer

Role Overview

• 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.

Requirements

• 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) plus the supporting cast (IAM, Lambda, ECS, S3, Secrets Manager, VPC).
• 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.

Tech Stack

• AWS
• Distributed Systems
• Docker
• Kubernetes
• Python
• Terraform

Benefits

• 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!