Machine Learning Engineer, AWS

🔥 26 minutes ago

🌪️ Oklahoma – Remote

🌪️ Oklahoma – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

☁️ Cloud Engineer

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CCT

WebsiteLinkedInAll Job Openings

51 - 200 employees

🏨 Hospitality

🍽️ Food & Beverage

📦 Logistics

Hospitality • Food & Beverage • Logistics

CCT is a B2B software company that provides Casino Insight, a cloud platform built exclusively for casinos to automate cash management, revenue audits, cage operations, and property-wide analytics. Their solutions replace manual, paper-based processes with unified data, automated workflows, and near-real-time insights across gaming, hotel, F&B, and retail operations, helping casinos reduce risk, improve efficiency, and increase profitability. CCT supports deployment and ongoing customer success, delivering measurable ROI and operational savings for casino operators.

📋 Description

• Build and maintain reproducible model training workflows on AWS using SageMaker, S3, Glue, and related services • Deploy and operate real-time and batch inference services with CI/CD, versioning, and canary, shadow, or A/B rollout strategies • Instrument production models for performance, data drift, latency, and errors, and automate retraining triggers • Maintain model lineage, auditability, and traceability for compliance, governance, and reporting in the regulated gaming industry • Enforce least-privilege IAM, encryption, and secure data access patterns across the ML platform • Optimize infrastructure and balance batch versus real-time workloads to reduce platform costs while maintaining reliability • Collaborate with engineers, data scientists, and product teams to translate business problems into ML solutions • Explore 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 scikit-learn, PyTorch, XGBoost, or equivalent ML frameworks • Experience building and maintaining CI/CD pipelines for ML systems • Ability to monitor and debug production ML systems for latency, drift, errors, and data quality, and identify root causes • Comfort with SQL and structured data at scale • Ability to collaborate across technical and non-technical teams and communicate clearly • Track record of self-directed learning and technical growth in AWS, ML frameworks, or deployment patterns