Senior Machine Learning Operations Engineer

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 (SageMaker Batch Transform, Snowflake Cortex / Snowpark ML, dbt-orchestrated scoring) and real-time inference (SageMaker 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 (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.

• 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. (required)
• 5+ years shipping software in production - Python, Docker, Kubernetes or ECS, CI/CD, distributed systems debugging - including time on-call. (required)
• 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. (required)
• AWS depth across the SageMaker surface (Training, Endpoints, Batch Transform, Model Registry, Pipelines) plus the supporting cast (IAM, Lambda, ECS, S3, Secrets Manager, VPC). (required)
• Snowflake fluency - Snowpark ML, Cortex, dbt-orchestrated batch scoring, RBAC for ML workloads. (required)
• IaC for ML - Terraform + SageMaker Pipelines or equivalent. No manual console deployments to production. (required)
• Feature store experience - SageMaker Feature Store, Tecton, or Feast - with explicit ownership of online/offline parity. (required)
• Champion/challenger, shadow, and canary deployment patterns as production muscle, not blog-post familiarity. (required)
• Drift and model monitoring - Evidently, Arize, WhyLabs, or SageMaker Model Monitor - wired to a paging path. (required)
• Software-engineering-first mindset - you treat ML systems as systems, not notebooks. (required)
• 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). (nice-to-have)
• Snowflake-native ML - Snowpark Container Services, Cortex AISQL, Cortex Agents - for workloads that do not need to leave the warehouse. (nice-to-have)
• Streaming feature engineering - Kafka, Flink, or Snowpipe Streaming - for sub-second features. (nice-to-have)
• Fine-tuning experience - LoRA, QLoRA, instruction tuning, eval-driven iteration - with an honest read on when fine-tuning beats prompting. (nice-to-have)
• A track record of shipping more with AI in the engineering loop than without. (nice-to-have)
• Regulated-industry experience (gaming, fintech, healthcare) - comfort with model risk, audit, and lineage requirements. (nice-to-have)

• 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!

BetMGM is a leading US sports betting and online gaming operator, formed in 2018 as a joint venture between MGM Resorts International and Entain. Headquartered in Jersey City, New Jersey, it holds exclusive access to MGM's land-based and online sports betting, poker and casino businesses across the United States and Canada. The company operates the BetMGM, Borgata Casino, Party Casino and Party Poker brands and is one of the largest operators in the regulated North American market. Its products combine online sportsbook, casino and poker with MGM's nationwide network of casinos.

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