BetMGM

Senior Machine Learning Operations Engineer

Vaga remota de Senior MLOps Engineer com fit claro de localização do candidato.

Publicada13 de jul. de 2026

Países elegíveis1 país aceito

Sinal de senioridadeSenior

Modelo de trabalhoRemoto

Locais aceitos para candidatos

Estados Unidos

Resumo da vaga

Conteúdo da vaga extraído em seções para revisão mais rápida.

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!
GenAI in production — Bedrock, Anthropic, or OpenAI APIs integrated into live systems; RAG pipelines; vector DBs (Snowflake Cortex Search,pgvector, Pinecone); evaluation frameworks (Langfuseor 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, orSnowpipeStreaming — 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 recordofshipping morewith AI in the engineering loop than without.
Regulated-industry experience (gaming, fintech, healthcare) — comfort with model risk, audit, and lineage requirements.

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.

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.

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