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.
Key Skills for This Role
SageMakerSageMaker TrainingSageMaker Model MonitorSageMaker Feature StoreSageMaker PipelinesSageMaker Batch Transform
Full Job Posting
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 (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.
Responsibilities
ML Production Platform
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.
Apply for this job in 1 click
Skip the repetitive application forms
Install the Base Career Chrome Extension and autofill job applications across major job boards with your profile.
Trusted by over 500,000 job seekers on Base Career
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.
CI/CD and Deployment Patterns
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.
Monitoring, Drift & Reliability
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.
Cost and Performance
Right-size endpoints, batch caching, request batching, and autoscaling. State cost-per-prediction targets up front and meet them.
GenAI Integration (Plus, Not Required)
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.
AI in the Engineering Loop
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.
Collaboration
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.
Qualifications
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.
Must-Haves
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.
Nice-to-Haves
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.
About Betmgminc
Verified company details for this employer are not available yet.
