Note: The job is a remote job and is open to candidates in USA. CCT is the creator of Casino Insight™, the award-winning platform trusted by more than 350 casinos worldwide. They are seeking a Machine Learning Engineer to design, deploy, and operate production ML systems on Amazon Web Services, owning the full lifecycle of ML services from training to deployment.
Responsibilities
- Build and maintain reproducible model training workflows on AWS (SageMaker, S3, Glue, etc.), making retraining, rollback, and experimentation routine rather than heroic
- 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
- Instrument production models for performance, data drift, latency, and errors — and automate retraining triggers when models drift out of tolerance
- Maintain model lineage, auditability, and traceability to meet the compliance, governance, and reporting needs of the regulated gaming industry
- Enforce least-privilege IAM, encryption, and secure data access patterns across the entire ML platform
- 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
- Collaborate with engineers, data scientists, and product teams to translate business problems into ML solutions, communicate tradeoffs clearly, and iterate based on feedback
- Continuously explore new AWS services, ML frameworks, and deployment patterns to improve reliability, observability, and developer velocity on the ML platform
Skills
- 3+ years of experience in machine learning engineering, MLOps, or a closely related discipline
- Hands-on experience with AWS ML and data services — SageMaker (training, endpoints, pipelines), S3, Lambda, Step Functions, CloudWatch, 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 (scikit-learn, PyTorch, XGBoost, or equivalent)
- Experience building and maintaining CI/CD pipelines for ML systems
- Demonstrated ability to monitor and debug production ML systems — latency, drift, errors, and data quality — and drive issues to root cause
- Comfort with SQL and working with structured data at scale
- Able to work collaboratively across teams, assume positive intent, and communicate clearly with both technical and non-technical stakeholders
- Track record of self-directed learning and technical growth in areas like AWS, ML frameworks, or deployment patterns
- Experience in a regulated industry (gaming, finance, healthcare) where auditability, explainability, and compliance are first-class concerns
- Familiarity with feature stores, model registries, or ML metadata tools (e.g., MLflow, SageMaker Model Registry)
- Experience with infrastructure-as-code (Terraform, CDK, or CloudFormation)
- Exposure to data drift detection libraries or custom drift monitoring implementations
Company Overview
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