• Model Development & Data Strategy: Lead development of machine learning models and algorithms to improve our search ranking and how we match consumers with pros. • Model Deployment & MLOps: Implement robust MLOps practices to ensure the seamless deployment and scalability of machine learning models. • Collaboration with Cross-Functional Teams: Work closely with a strong team of engineers, data scientists, product managers, and designers to build scalable and high-impact machine learning systems. • Innovation: Foster innovation within the team, exploring new approaches and techniques to solve complex business problems. • Mentorship: Guide junior team members and foster a culture of continuous learning and technical excellence.

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.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description Locked On Podcast Network is seeking Sports Podcast Hosts to lead and produce daily podcasts focused on professional and college sports teams. - Host and produce a 30-minute podcast episode five days a week (Monday through Friday). - Broadcast shows live on YouTube and distribute them across all major podcast platforms as well as OTT platforms (VOD and FAST Channels). - Engage with fans daily, fostering a community around your team. - Provide insightful analysis, updates, and commentary that serves as a trusted source for fans. Qualifications - A strong passion for sports (NFL, NBA, MLB, NHL, or College). - Excellent communication skills with an engaging personality. - Experience in podcasting or broadcasting is preferred but not required. - The ability to connect with fans and create content that resonates with diverse audiences. Benefits - TEGNA offers comprehensive benefits designed to safeguard the physical, mental and financial health of our employees and their families. - Two medical plan options for full and part-time employees through Blue Cross Blue Shield of Texas. - Access to dental and eye care coverage; fertility, surrogacy and adoption assistance; disability and life insurance. - 401(k) program with a 100% match from the company on contributions made up to the first 4% of pay. - Regardless of participation in TEGNA medical plans, ALL employees and their eligible family members receive nine free virtual doctor’s appointments with a physician through Teladoc, and 12 free annual therapy sessions with a licensed clinician through Spring Health. - A generous Paid Time Off (PTO) benefit as well as nine paid holidays per year. - * Some jobs are covered by a collective bargaining agreement and thus some or all of the benefits described herein may not apply. Company Description TEGNA Inc. (NYSE: TGNA) helps people thrive in their local communities by providing the trusted local news and services that matter most. - With 64 television stations in 51 U.S. markets, TEGNA reaches more than 100 million people monthly across the web, mobile apps, streaming, and linear television. - Together, we are building a sustainable future for local news.

• 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