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Machine Learning Engineer (AWS)
Location
United States
Posted
99 days ago
Salary
0
Seniority
Mid Level
Job Description
Machine Learning Engineer (AWS)
CCT
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.
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Machine Learning Engineer
Smart WorkingEmpowering companies to work with the best engineers in the world
• Architect, implement, and maintain production-grade, low-latency ML services for ranking, recommendation, and forecasting use cases • Collaborate with data scientists, product managers, and engineers to identify the best technical approaches to product and infrastructure challenges • Design and support experimentation frameworks to test hypotheses and measure improvements to models • Advise on data strategy, ensuring high-quality, well-structured datasets are available for current and future data science initiatives • Deliver machine learning models that meet agreed engineering standards, ensuring scalability, resilience, and long-term maintainability • Enhance and evolve an AWS-native MLOps platform, supporting high availability and low-latency inference • Monitor, maintain, and continuously improve deployed models in production environments • Contribute positively to team culture, demonstrating curiosity, ownership, and a bias toward learning and improvement
• Understand business cases to evaluate potential solutions, leveraging ML and AI to address complex challenges • Analyze large, complex data sets to translate them into actionable insights • Simplify data sets to solve a wide array of challenging problems using analytical and statistical approaches • Perform the preparation and preprocessing of both structured and unstructured data to ensure model accuracy and effectiveness • Design, develop, implement, and test both descriptive and predictive ML models, focusing on quality, accuracy, and consistency • Convert data into meaningful insights and present information using advanced data visualization tools and techniques • Deploy models into production environments and be responsible for model management (MLOps) • Mentor machine learning engineers and junior machine learning engineers in the AI/ML team.
• Define and own the ML strategy and technical roadmap for Gather AI, aligned with product and business objectives • Lead and grow the Machine Learning and FPT teams, establishing a culture of rigor, experimentation, and production-quality delivery • Drive improvements to core computer vision models (object detection, segmentation, OCR) used across our drone and MHE Vision products • Build out MLOps infrastructure — model training pipelines, deployment, monitoring, and CI/CD for ML workloads • Collaborate with the Director of Cloud Engineering and Director of Autonomy to ensure ML systems integrate seamlessly into the broader platform • Partner with Product and Operations to translate customer needs into ML-driven product capabilities
