• 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.

• Design, develop, and optimize machine learning models in one or more solutions, including asset generation and capture, render enhancement, scene intelligence, agentic design workflows, and intuitive design interactions. • Investigate and bring techniques from a variety of AI research areas, such as diffusion, super-resolution, conditioned generation, plus neural and differentiable rendering, into artists’ hands. • Evaluate, integrate, and orchestrate off-the-shelf third-party foundation models to accelerate feature development and deployment. • Mentor other engineers and contribute to the growth of the team’s knowledge and expertise in machine learning. • Collaborate with cross-functional teams and our ML Product Manager to define the product requirements and scope of delivery of solutions to product teams. • Work closely with our MLOps Engineer to develop and maintain pipelines for distributed training, inference optimization/quantization/serving, experiment tracking, model versioning & validation, and deployment to the cloud (AWS/Azure/GCP). • Implement appropriate model evaluation tests, data curation processes, and apply dataset-rights awareness, and responsible AI/governance. • Stay updated and share knowledge on the latest developments in machine learning, generative AI, natural language processing, and 3D visualization, and implement cutting-edge techniques to enhance our solutions. • Ensure high-quality code and documentation, following best practices in software development and machine learning.

• Design, develop, and optimize scalable Machine Learning models for advertising intelligence and forecasting systems • Analyze large-scale historical and real-time datasets to improve forecasting accuracy and monetization strategies • Build and maintain distributed data processing pipelines using Spark and related Big Data technologies • Develop production-ready ML solutions using Python within AWS cloud infrastructure • Research, evaluate, and implement Machine Learning algorithms suitable for high-load AdTech environments • Improve model performance, scalability, reliability, and operational efficiency • Collaborate with Data Engineers, Product Managers, and distributed engineering teams to deliver end-to-end ML solutions • Contribute to ML architecture decisions, experimentation approaches, and engineering best practices • Monitor, validate, and optimize ML model quality and forecasting performance in production environments • Produce technical documentation related to ML pipelines, models, and distributed systems • Mentor engineers and support technical growth within the team • Drive innovation in Machine Learning and AdTech technologies

• Design, develop, and optimize scalable Machine Learning models for advertising intelligence and forecasting systems • Analyze large-scale historical and real-time datasets to improve forecasting accuracy and monetization strategies • Build and maintain distributed data processing pipelines using Spark and related Big Data technologies • Develop production-ready ML solutions using Python within AWS cloud infrastructure • Research, evaluate, and implement Machine Learning algorithms suitable for high-load AdTech environments • Improve model performance, scalability, reliability, and operational efficiency • Collaborate with Data Engineers, Product Managers, and distributed engineering teams to deliver end-to-end ML solutions • Contribute to ML architecture decisions, experimentation approaches, and engineering best practices • Monitor, validate, and optimize ML model quality and forecasting performance in production environments • Produce technical documentation related to ML pipelines, models, and distributed systems • Mentor engineers and support technical growth within the team • Drive innovation in Machine Learning and AdTech technologies