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Machine Learning Engineer (AWS)
Job Location:
Monthly Salary:
Posted:
3 July 2026 (30+ days ago)
Posted:
Application Deadline:
5 October 2026
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Summary
Were looking for a Machine Learning Engineer to design deploy and operate production ML systems on Amazon Web Services. Youll own the full lifecycle in a real-world high-stakes environment from training and packaging through deployment monitoring retraining security and cost control.
This role sits at the intersection of ML engineering and MLOps and is core to CCTs analytics strategy. Youll partner closely with data scientists engineers and product stakeholders to turn complex time-series and transactional data into reliable observable and cost-effective ML services that our customers can trust.
Youll thrive here if you naturally dig into why models behave the way they do enjoy tracing issues to their root cause and like collaborating across disciplines to ship robust systems that are built to last.
What Youll Do
- 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.
Requirements
- 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.
Certified Banana Picker
Nice to Have
- 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.
Success Looks Like
- Production models run reliably with clear measurable business impact for casino operators.
- Failures are observable recoverable and explainable with logs metrics and traces that tell the full story.
- ML systems scale predictably with usage and data volume without runaway cost.
- The ML platform becomes a trusted well-understood part of CCTs product ecosystem for both internal teams and external customers.
About CCT
CCT is the creator of Casino Insight the award-winning platform trusted by more than 350 casinos worldwide to automate cage operations revenue audits and operational analysis. Since 2012 Casino Insight has helped casinos replace manual work with streamlined workflows improving accuracy compliance and profitability.
Headquartered in Tulsa Oklahoma CCT integrates seamlessly with leading casino management hospitality and financial systemsdelivering measurable ROI and empowering teams to work smarter at every level.
We may use artificial intelligence (AI) tools to support parts of the hiring process such as reviewing applications analyzing resumes or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed please contact us.
Required Experience:
IC
About Company
About CCT CCT is the creator of Casino Insight™, the award-winning platform trusted by more than 350 casinos worldwide to automate cage operations, revenue audits, and operational analysis. Since 2012, Casino Insight has helped casinos replace manual work with streamlined workflows, i ... View more
