Cloud Engineer

• 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.

Machine Learning Engineer, AWS

Analytics Engineer

• 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.
• 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.

Full Stack Software Engineer – Insight Analytics

• 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, and allow or prevent commits or PR merges based on predefined quality thresholds.

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Machine Learning Engineer, AWS

Full Stack Software Engineer – Insight Analytics

AWS Cloud Data Engineer