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Machine Learning Engineer (AWS)

1 week, 6 days ago
United States
Mid Level
Machine Learning Engineer
Software Development
Apache Airflow AWS CI/CD CloudFormation Encryption Feature Engineering Machine Learning MLflow MLOps Python PyTorch SageMaker 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 3

Description

  • Design, build, and maintain reproducible model training workflows on AWS using services such as SageMaker, S3, and Glue.
  • Deploy and operate real-time and batch inference services with CI/CD pipelines, versioning, and safe rollout strategies such as canary, shadow, and A/B deployments.
  • Monitor production models for performance, data drift, latency, errors, and data quality, and automate retraining when models drift beyond acceptable thresholds.
  • Maintain model lineage, auditability, and traceability to support compliance, governance, and reporting requirements in a regulated industry.
  • Enforce secure ML platform practices, including least-privilege IAM, encryption, and secure data access patterns.
  • Optimize infrastructure and manage cost as a core engineering metric while preserving reliability.
  • Collaborate with engineers, data scientists, and product stakeholders to translate business problems into ML solutions and communicate tradeoffs clearly.
  • Continuously evaluate new AWS services, ML frameworks, and deployment patterns to improve reliability, observability, and developer velocity.

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 common ML frameworks such as scikit-learn, PyTorch, and XGBoost, or equivalent.
  • Experience building and maintaining CI/CD pipelines for ML systems.
  • Demonstrated ability to monitor and debug production ML systems, including latency, drift, errors, and data quality, and drive issues to root cause.
  • Comfort with SQL and working with structured data at scale.
  • Ability to work collaboratively across teams and communicate clearly with both technical and non-technical stakeholders.
  • Track record of self-directed learning and technical growth in areas such as AWS, ML frameworks, or deployment patterns.
  • Preferred: experience in a regulated industry such as gaming, finance, or healthcare where auditability, explainability, and compliance are important.
  • Preferred: familiarity with feature stores, model registries, or ML metadata tools such as MLflow or SageMaker Model Registry.
  • Preferred: experience with infrastructure-as-code tools such as Terraform, CDK, or CloudFormation.
  • Preferred: exposure to data drift detection libraries or custom drift monitoring implementations.

Interested in this position?

Apply directly on the company website

Apply Now

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