CCT

• Own the Insight Cash roadmap, balancing customer commitments, platform migration, and longer-term strategic investments • Maintain a clear prioritization framework and surface tradeoffs so stakeholders understand how decisions get made • Translate business goals and customer needs into a product vision the team can rally around • Lead ongoing discovery through customer conversations, interviews, field visits, and feedback from Client Solutions and Support • Become the internal authority on casino cage and cash operations workflows • Partner with Engineering and QA to define, refine, and deliver features, writing clear requirements and maintaining a healthy backlog • Drive release planning across Product, Engineering, QA, and Client Solutions • Evaluate where AI capabilities can create real value in Insight Cash, and champion AI tooling across the development lifecycle

• Design and ship full-stack features across our TypeScript frontend and TypeScript Lambda backends. • Model data and metrics in Cube.dev; build embedded analytics experiences using Embeddable components. • Own the production lifecycle of your features: CI/CD, performance, observability, and post-deploy validation. • Implement your own AWS infrastructure (Lambda, IAM, networking, storage) using Terraform; we have a dedicated AWS engineer who will review and partner with you on the harder pieces. • Work day-to-day in Claude Code and contribute to the team's AI context library, prompts, and process improvements. • Pair with our designer, data engineers, and dashboard configuration lead to land features that hold up against real customer data.

• Serve as a shared resource across product teams to provide expertise and scalable solutions for data integration, modeling, and analytics across all products. • Design and implement scalable data pipelines using AWS services such as AWS Glue, AWS Data Pipeline, and Amazon Kinesis. • Create and maintain all Infrastructure-as-Code (IaC) for primary product teams using tools like AWS CloudFormation or CDK. • Develop and maintain data storage solutions using Amazon S3, Redshift, RDS, DynamoDB, or other AWS-managed databases. • Design and develop code pipelines for our primary product application to: • Build application components. • Run automated tests created by the QA team. • Execute static code analysis. • Allow or prevent commits or PR merges based on predefined quality thresholds. • Deploy and manage AWS infrastructure resources as needed to support organizational and product-specific goals. • Implement monitoring, logging, and centralization of application instrumentation for real-time insights and troubleshooting. • Optimize ETL workflows to efficiently manage data across diverse sources and destinations. • Set up and manage AWS IAM policies, roles, and security-related configurations to ensure secure access and data protection. • Stay updated on AWS innovations and recommend tools or best practices to enhance the organization’s data ecosystem. • Create comprehensive documentation and provide training for AWS-based data solutions, ensuring knowledge transfer and ease of use. • Ensure all data processes and systems adhere to security best practices, aligning with OWASP Top 10, CWE Top 25 guidelines, and CIS AWS Foundations Benchmark.

We’re looking for a Machine Learning Engineer to design, deploy, and operate production ML systems on Amazon Web Services. You’ll 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 CCT’s analytics strategy. You’ll 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. You’ll 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 You'll 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. 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 CCT’s product ecosystem — for both internal teams and external customers. Since 2012, CCT (cct.io) has helped more than 350 casinos worldwide streamline workflows, simplify compliance, and improve profitability. With an award-winning suite of software and services, we provide scalable solutions that integrate with more than 100 casino management, hospitality, and financial systems. We are a team of 120 (and growing!) headquartered in Tulsa, OK, with remote and WFH locations across North America. Our core values represent who we are and how we work — Customer Oriented, Problem Solving, Driven, Adaptability, and Teamwork.

• Shape how casino leaders understand and act on their data by designing clear, unified views across gaming, hotel, food & beverage, and retail • Lead design reviews and articulate the strategic rationale behind design decisions • Design and prototype end-to-end workflows for high-stakes operations—reporting, cash management, and auditing • Interview users and run user tests to deeply understand user needs and validate solutions • Review user feedback, on-site observations, product analytics and stakeholder goals to identify opportunities to enhance user experience