Senior Machine Learning Operations Engineer

Benefits Medical, Dental, Vision, Life, and Disability Insurance 401(k) with company match Pre‑tax spending accounts (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 About the Role 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 and real‑time inference. They build 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. Responsibilities ML Production Platform – Stand up and operate BetMGM's ML platform on AWS and Snowflake with Terraform‑managed infrastructure, build self‑service scaffolds for data scientists. Batch and Real‑Time Inference – Design and operate batch scoring pipelines and real‑time inference paths with SLAs for freshness, cost, and latency. CI/CD and Deployment Patterns – Build CI/CD pipelines, implement champion‑challenger, shadow deployments, and canary releases. Monitoring, Drift & Reliability – Set up drift detection, data quality, model performance monitoring, and incident response with paging routes. Cost and Performance – Right‑size endpoints, batch caching, request batching, autoscaling, and meet cost‑per‑prediction targets. GenAI Integration (Optional) – Integrate LLM APIs into production paths. AI in the Engineering Loop – Direct AI coding agents across infrastructure code and model‑serving glue. Collaboration – Partner with data engineering, analytics partners, Entain India, and contractor ML partners. Qualifications BS or MS in Computer Science, Math, Statistics, Machine Learning, or related STEM field (or equivalent experience). PhD is not required. 5+ years shipping software in production: Python, Docker, Kubernetes or ECS, CI/CD, distributed systems debugging, on‑call experience. 3+ years operating ML in production with real traffic, latency, cost budgets, and runbooks. Deep AWS knowledge (SageMaker, IAM, Lambda, ECS, S3, Secrets Manager, VPC). Snowflake fluency (Snowpark ML, Cortex, dbt, RBAC). IaC for ML: Terraform + SageMaker Pipelines or equivalent. Feature store experience with online/offline parity. Champion/challenger, shadow, and canary deployment patterns in production. Drift and model monitoring tools (Evidently, Arize, WhyLabs, or SageMaker Model Monitor) with paging. Software‑engineering‑first mindset; treat ML systems as software systems. Nice‑to‑Haves GenAI in production (Bedrock, Anthropic, OpenAI APIs, RAG pipelines, vector DBs). Snowflake‑native ML (Snowpark Container Services, Cortex AISQL, Cortex Agents). Streaming feature engineering (Kafka, Flink, Snowpipe Streaming). Fine‑tuning experience (LoRA, QLoRA, instruction tuning, eval‑driven iteration). Track record of shipping more with AI in the engineering loop. Regulated‑industry experience (gaming, fintech, healthcare). Compensation Annual salary range: $135,000 to $170,000. Eligible for a performance‑based bonus plan. Location may affect starting pay. Eligibility Legal authorization to work in the U.S. without immigration sponsorship. Position is not eligible for H‑1B, O‑1, E‑3, TN, OPT, etc. Gaming Compliance & Licensing Requirements Must be licensed by at least one jurisdictional agency; certain positions require multiple licenses. Failure to maintain licensure may result in termination. Background checks and responsible gambling policies apply. Equal Employment Opportunity BetMGM LLC is an Equal Opportunity Employer. We provide equal employment opportunities to all qualified individuals regardless of race, religion, gender, age, marital status, national origin, sexual orientation, citizenship status, veteran status, disability, or any other legally protected status. #J-18808-Ljbffr