Machine Learning Engineer (AWS)

{
  "content_hash": "bdbc675b801604946a238593efbfa32e98007a8046411005596bab61fbd3ac76",
  "source_hash": "9b87f1e90b0365c92285f35edc3211b324678de91b5bdbdf3f877e2efe842c51",
  "last_changed_at": "2026-06-17T07:55:14.907Z",
  "active_status": "active"
}

{
  "language": "en",
  "location": {
    "raw": "Tulsa",
    "city": "Tulsa",
    "region": null,
    "country": "United States",
    "is_remote": true,
    "confidence": 0.9
  },
  "salary_max": null,
  "salary_min": null,
  "inferred_at": "2026-06-18T07:55:29.001Z",
  "launch_scope": {
    "reason": "english_us_canada",
    "included": true,
    "language": "en",
    "location": {
      "raw": "Tulsa",
      "city": "Tulsa",
      "region": null,
      "country": "United States",
      "is_remote": true,
      "confidence": 0.9
    },
    "countries": [
      "United States"
    ]
  },
  "remote_policy": "remote",
  "salary_period": null,
  "workplace_type": "remote",
  "salary_currency": null
}

{}

{
  "lists": [
    {
      "text": "What You'll Do",
      "content": "\n<li>Build and maintain reproducible model training workflows on AWS (SageMaker, S3, Glue, etc.), making retraining, rollback, and experimentation routine rather than heroic.</li>\n<li>Deploy and operate real-time and batch inference services with full CI/CD pipelines, versioning, and safe rollout strategies (canary, shadow, A/B) so changes are deliberate and observable.</li>\n<li>Instrument production models for performance, data drift, latency, and errors — and automate retraining triggers when models drift out of tolerance.</li>\n<li>Maintain model lineage, auditability, and traceability to meet the compliance, governance, and reporting needs of the regulated gaming industry.</li>\n<li>Enforce least-privilege IAM, encryption, and secure data access patterns across the entire ML platform.</li>\n<li>Treat cost as a first-class engineering metric — right-size infrastructure, balance batch vs. real-time workloads, and continually reduce platform spend without sacrificing reliability.</li>\n<li>Collaborate with engineers, data scientists, and product teams to translate business problems into ML solutions, communicate tradeoffs clearly, and iterate based on feedback.</li>\n<li>Continuously explore new AWS services, ML frameworks, and deployment patterns to improve reliability, observability, and developer velocity on the ML platform.</li>\n"
    },
    {
      "text": "Requirements",
      "content": "\n<li>3+ years of experience in machine learning engineering, MLOps, or a closely related discipline.</li>\n<li>Hands-on experience with AWS ML and data services — SageMaker (training, endpoints, pipelines), S3, Lambda, Step Functions, CloudWatch, MWAA (Apache Airflow).</li>\n<li>Experience working with time series data, including feature engineering, seasonality handling, and temporal train/test splits.</li>\n<li>Strong Python skills and familiarity with common ML frameworks (scikit-learn, PyTorch, XGBoost, or equivalent).</li>\n<li>Experience building and maintaining CI/CD pipelines for ML systems.</li>\n<li>Demonstrated ability to monitor and debug production ML systems — latency, drift, errors, and data quality — and drive issues to root cause.</li>\n<li>Comfort with SQL and working with structured data at scale.</li>\n<li>Able to work collaboratively across teams, assume positive intent, and communicate clearly with both technical and non-technical stakeholders.</li>\n<li>Track record of self-directed learning and technical growth in areas like AWS, ML frameworks, or deployment patterns.</li>\n\n<div><span style=\"color: #f5f5f5;\">Certified Banana Picker</span>&nbsp;</div>\n<div>&nbsp;</div>"
    },
    {
      "text": "Nice to Have",
      "content": "\n<li>Experience in a regulated industry (gaming, finance, healthcare) where auditability, explainability, and compliance are first-class concerns.</li>\n<li>Familiarity with feature stores, model registries, or ML metadata tools (e.g., MLflow, SageMaker Model Registry).</li>\n<li>Experience with infrastructure-as-code (Terraform, CDK, or CloudFormation).</li>\n<li>Exposure to data drift detection libraries or custom drift monitoring implementations.</li>\n"
    },
    {
      "text": "Success Looks Like",
      "content": "\n<li>Production models run reliably with clear, measurable business impact for casino operators.</li>\n<li>Failures are observable, recoverable, and explainable — with logs, metrics, and traces that tell the full story.</li>\n<li>ML systems scale predictably with usage and data volume, without runaway cost.</li>\n<li>The ML platform becomes a trusted, well-understood part of CCT’s product ecosystem — for both internal teams and external customers.</li>\n"
    }
  ],
  "country": "US",
  "createdAt": 1772563498668,
  "updatedAt": null,
  "categories": {
    "team": "Technology",
    "location": "Tulsa",
    "department": "Product & Technology",
    "allLocations": [
      "Tulsa"
    ]
  },
  "salaryRange": null,
  "workplaceType": "remote"
}