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Machine Learning Engineer (AWS)

1 day, 8 hours ago
United States
Full-time
Mid Level
Machine Learning Engineer
Software Development
Apache Airflow AWS AWS CDK CI/CD CloudFormation Encryption Feature Engineering MLflow MLOps Python PyTorch Scikit-learn SQL Terraform XGBoost
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Casino Cash Trac

Casino Cash Trac

Casino Cash Trac offers a comprehensive platform designed specifically for casinos, integrating cash management, analytics, and operational intelligence to enhance efficiency and decision-making across various departments.

Internet Software & Services
Founded 2012
View All Jobs 4

Description

  • Build and maintain reproducible model training workflows on AWS.
  • Deploy and operate real-time and batch inference services with CI/CD, versioning, and safe rollout strategies.
  • Instrument production models for performance, data drift, latency, and errors.
  • Automate retraining triggers when models drift out of tolerance.
  • Maintain model lineage, auditability, and traceability for compliance and governance needs.
  • Enforce least-privilege IAM, encryption, and secure data access patterns across the ML platform.
  • Manage infrastructure cost by right-sizing workloads and reducing platform spend without sacrificing reliability.
  • Collaborate with engineers, data scientists, and product teams to translate business problems into ML solutions.
  • Continuously evaluate new AWS services, ML frameworks, and deployment patterns to improve the platform.

Requirements

  • 3+ years of experience in machine learning engineering, MLOps, or a closely related discipline.
  • Hands-on experience with AWS ML and data services, including SageMaker, S3, Lambda, Step Functions, CloudWatch, and MWAA (Apache Airflow).
  • Experience working with time series data, including feature engineering, seasonality handling, and temporal train/test splits.
  • Strong Python skills and familiarity with ML frameworks such as scikit-learn, PyTorch, or XGBoost.
  • Experience building and maintaining CI/CD pipelines for ML systems.
  • Experience monitoring and debugging production ML systems, including latency, drift, errors, and data quality.
  • Comfort with SQL and structured data at scale.
  • Ability to collaborate across teams and communicate clearly with technical and non-technical stakeholders.
  • Track record of self-directed learning and technical growth in AWS, ML frameworks, or deployment patterns.
  • Experience in a regulated industry such as gaming, finance, or healthcare is preferred.
  • Familiarity with feature stores, model registries, or ML metadata tools such as MLflow or SageMaker Model Registry is preferred.
  • Experience with infrastructure-as-code tools such as Terraform, CDK, or CloudFormation is preferred.
  • Exposure to data drift detection libraries or custom drift monitoring implementations is preferred.

Interested in this position?

Apply directly on the company website

Apply Now

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