Senior Machine Learning Operations Engineer

BetMGM is revolutionizing sports betting and online gaming in the United States and Canada. They are seeking a Senior Machine Learning Operations Engineer to own the path from a trained model to a production endpoint, ensuring it meets latency, cost, and reliability budgets.

Responsibilities:

• 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
• Partner with the Helix team on AI personalization workloads as they ramp toward March Madness 2027
• Direct AI coding agents (Claude Code, Cursor, GitHub 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 land the right interfaces between research and production — opinionated about the boundary
• Coordinate with Entain India and contractor ML partners as workloads consolidate onto the BetMGM-owned platform

Requirements:

• BS or MS in Computer Science, Math, Statistics, Machine Learning, or other STEM field — or equivalent practical experience. Practical experience wins ties; a PhD is neither required nor a tiebreaker
• 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
• GenAI in production — Bedrock, Anthropic, or OpenAI APIs integrated into live systems; RAG pipelines; vector DBs (Snowflake Cortex Search, pgvector, Pinecone); evaluation frameworks (Langfuse or in-house)
• Snowflake-native ML — Snowpark Container Services, Cortex AISQL, Cortex Agents — for workloads that do not need to leave the warehouse
• Streaming feature engineering — Kafka, Flink, or Snowpipe Streaming — for sub-second features
• Fine-tuning experience — LoRA, QLoRA, instruction tuning, eval-driven iteration — with an honest read on when fine-tuning beats prompting
• A track record of shipping more with AI in the engineering loop than without
• Regulated-industry experience (gaming, fintech, healthcare) — comfort with model risk, audit, and lineage requirements